Robotic system control method and controller

ABSTRACT

The present disclosure provides a control method of a robotic system. The control method includes: deriving an approach location at which the end effector grips an operation object; deriving a scan location for scanning an identifier of the operation object; and based on the approach location and the scan location, creating or deriving a control sequence to instruct the robot to execute the control sequence. The control sequence includes (1) gripping the operation object from a start location; (2) scanning an identifier of the operation object with a scanner located between the start location and a task location; (3) temporarily releasing the operation object from the end effector and regripping the operation object by the end effector to be shifted, at a shift location, when a predetermined condition is satisfied; and (4) moving the operation object to the task location.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.17/110,205 filed Dec. 2, 2020, now allowed, which is a continuation ofU.S. patent application Ser. No. 16/752,582 filed Jan. 24, 2020 (nowU.S. Pat. No. 10,870,204), which is a continuation-in-part of, andclaims the benefit of U.S. patent application Ser. No. 16/743,313 filedJan. 15, 2020 (now U.S. Pat. No. 10,933,527), which is a continuationof, and claims the benefit of U.S. patent application Ser. No.16/546,209, filed Aug. 20, 2019 (now U.S. Pat. No. 10,569,416), which isa continuation of, and claims the benefit of U.S. patent applicationSer. No. 16/258,120, filed Jan. 25, 2019 (now U.S. Pat. No. 10,456,915),which are incorporated by reference herein in their entireties.

This application contains subject matter related to U.S. patentapplication Ser. No. 16/546,226, filed Aug. 20, 2019 (now U.S. Pat. No.10,596,701), U.S. patent application Ser. No. 16/546,236, filed Aug. 20,2019 (now U.S. Pat. No. 10,569,417), and U.S. patent application Ser.No. 16/546,248, filed Aug. 20, 2019 (now U.S. Pat. No. 10,576,631), alltitled “A ROBOTIC SYSTEM WITH ENHANCED SCANNING MECHANISM,” andincorporated herein by reference in their entireties.

BACKGROUND OF THE INVENTION Field of the Invention

The present disclosure relates generally to a robotic system, and moreparticularly to a controller and a control method of a robotic systemthat manipulates an operation object such as an article, a distributionsystem, a program, and a medium.

Description of Related Art

With their ever-increasing performance and lowering cost, many robots(e.g., machines configured to automatically/autonomously executephysical actions) are now extensively used in many fields. Robots, forexample, can be used to execute various tasks such as manipulating ormoving of an operation object in manufacturing, assembly, packing,transfer, transport, and the like. In executing the tasks, the robotscan replicate human actions, thereby replacing or reducing dangerous orrepetitive human tasks.

As a system (robotic system) using such a robot, for example, JapanesePatent Application Laid-Open No. 2018-167950 discloses an automaticdistribution system. In order to automate and save labor fromwarehousing to delivering of an article, the automatic distributionsystem includes a carrying container storage mechanism that temporarilystores a carrying container; and an automatic article delivery mechanismin which articles in the carrying container are automatically collectedin a shipment container based on delivery information.

However, despite technological advancement, robots often lack thesophistication required for replicating human involvement task in orderto execute larger and/or more complex tasks. For this reason, automationand advanced functionality in robotic systems are not yet sufficient,and there are many tasks that are difficult to replace humaninvolvement, and robotic systems lack the granularity of control andflexibility in actions to be executed. Therefore, there is still needfor technical improvements to manage various actions and/or interactionsbetween robots and further promote automation and advanced functionalityof robotic systems. Therefore, an object of the present disclosure is toprovide a controller and a control method of a robotic system, and thelike that can realize a high degree of cooperation between unitsincluding a robot and can sufficiently increase a storage efficiency ofan operation object, for example.

SUMMARY OF THE INVENTION

The present invention employs the following configuration to solve theabove-described problems.

[1] According to the present disclosure, a control method of a roboticsystem that includes a robot having a robotic arm and an end effectorcomprises: deriving an approach location at which the end effector gripsan operation object; deriving a scan location for scanning an identifierof the operation object; and based on the approach location and the scanlocation, creating or deriving a control sequence to instruct the robotto execute the control sequence. The control sequence includes thefollowing (1) to (4):

(1) gripping the operation object at a start location;

(2) scanning identification information of the operation object (e.g., acomputer-readable identifier such as a barcode or a Quick Response (QR)code (registered trademark)) with a scanner located between the startlocation and a task location;

(3) temporarily releasing the operation object from the end effector andregripping the operation object by the end effector to be shifted, at ashift location, when a predetermined condition is satisfied; and

(4) moving the operation object to the task location.

Here, the “operation object” indicates an object to be manipulated bythe robot provided in the robotic system, and includes, for example, oneor more articles (items), a bin, a container, and/or a box in which thearticles are placed or stored. The containers may be packed or unpacked,and a part of the containers (e.g., an upper surface thereof) may beopened. In addition, in some embodiments and examples, the “operationobject” may be placed on a shelf, a pallet, a conveyor, and othertemporary placing places. The “control sequence” indicates an orderedset controls (e.g., commands and/or settings) for causing eachcorresponding robotic unit in the robotic system to execute anindividual task.

[2] In the above described configuration, the control sequence mayfurther include the following (5) and (6):

(5) setting a condition that a storage efficiency of the operationobject at the task location is increased in a case where the operationobject is shifted and a direction of gripping the operation object bythe end effector is changed, as the predetermined condition; and

(6) calculating a storage efficiency at the task location beforeshifting the operation object and a storage efficiency at the tasklocation after shifting the operation object.

[3] In the above described configuration, the control sequence mayfurther include the following (7) and (8):

(7) deriving a height of the operation object; and

(8) calculating the storage efficiency based on the height of theoperation object.

[4] In the above described configuration, the height of the operationobject may be calculated from a height location (level) of a top surfaceof the operation object and a height location (level) of a bottomsurface of the operation object measured in a state of being gripped bythe end effector.

[5] In the above described configuration, the height of the operationobject may be measured when the operation object is scanned with thescanner.

[6] In the above described configuration, the control sequence mayfurther include (9) temporarily releasing the operation object from theend effector by placing the operation object on a temporary placingtable, at the shift location, when the predetermined condition issatisfied.

[7] In the above described configuration, the control method may furtherinclude: deriving imaging data indicating a pick-up area including theoperation object; determining an initial pose of the operation objectbased on the imaging data; calculating a confidence measure indicating alikelihood that the initial pose of the operation object is accurate;and deriving the approach location and the scan location based on theconfidence measure.

Here, the “pose” indicates a location and/or an orientation of theoperation object (e.g., a posture including an orientation in a stoppedstate), and includes a translational component and/or a rotationalcomponent in a grid system utilized by the robotic system. In addition,the “pose” can be represented by a vector, a set of angles (e.g., Eulerangles and/or roll-pitch-yaw angles), a homogeneous transformation, or acombination thereof. In the “pose” of the operation object, a coordinatetransformation thereof and the like may include a translationalcomponent, a rotational component, changes thereof, or a combinationthereof.

In addition, the “confidence measure” indicates a quantified measurerepresenting a degree of certainty (a degree of a certainty or alikelihood) that a determined pose of the operation object matches anactual pose of the operation object in a real-world. In other words, the“confidence measure” may be a measure indicating an accuracy of adetermined pose of the operation object. The “confidence measure” may bereferred to as an index indicating a likelihood that a determined posematches an actual pose of the operation object. For example, the“confidence measure” can be a measure to be derived based on a result ofmatching between one or more visible characteristics of the operationobject (e.g., a shape, a color, an image, a design, a logo, a text, andthe like) in image data of a pick-up area including the operation objectand information regarding the visible characteristics of the operationobject stored in master data.

[8] In the above described configuration, the control sequence mayfurther include (10) selectively calculating the approach location andthe scan location according to a performance metric and/or a scanmetric, based on a result of comparing the confidence measure to asufficiency threshold, and the scan metric may be related to alikelihood that the identifier of the operation object is not covered bythe end effector, regardless of whether the initial pose of theoperation object is accurate or not.

[9] In the above described configuration, when the confidence measuredoes not satisfy the sufficiency threshold, the approach location andthe scan location may be derived based on the scan metric or may bederived based on the scan metric with prioritizing the scan metric overthe performance metric.

[10] Possibly, in the above described configuration, when the confidencemeasure satisfies the sufficiency threshold, the approach location andthe scan location may be derived based on the performance metric.

[11] In the above described configuration, the control sequence mayfurther include the following (11) and (12):

(11) deriving a first scan location for providing identificationinformation of the operation object to the scanner, and a second scanlocation for providing alternative identification information of theoperation object to the scanner; and

(12) moving the operation object to the task location and ignoring thesecond scan location in a case where a scan result indicates asuccessful scan, or moving the operation object to the second scanlocation in a case where the scan result indicates a failed scan, afterthe operation object is moved to the first scan location.

[12] In addition, according to the present disclosure, there is provideda non-transitory computer-readable medium storing processor instructionsfor performing a control method of a robotic system that includes arobot having a robotic arm and an end effector, in which the processorinstructions include an instruction for deriving an approach location atwhich the end effector grips an operation object; an instruction forderiving a scan location for scanning an identifier of the operationobject; and an instruction for creating or deriving a control sequenceto instruct the robot to execute the control sequence, based on theapproach location and the scan location. The control sequence includesthe following (1) to (4):

(1) gripping the operation object at a start location;

(2) scanning identification information of the operation object with ascanner located between the start location and a task location;

(3) temporarily releasing the operation object from the end effector andregripping the operation object by the end effector to be shifted, at ashift location, when a predetermined condition is satisfied; and

(4) moving the operation object to the task location.

[13] In the above described configuration, the control sequence mayfurther include the following (5) and (6):

(5) setting a condition that a storage efficiency of the operationobject at the task location is increased in a case where the operationobject is shifted and a direction of gripping the operation object bythe end effector is changed, as the predetermined condition; and

(6) calculating a storage efficiency at the task location beforeshifting the operation object and a storage efficiency at the tasklocation after shifting the operation object.

[14] In the above described configuration, the control sequence mayfurther include the following (7) and (8):

(7) deriving a height of the operation object; and

(8) calculating the storage efficiency based on the height of theoperation object.

[15] In the above described configuration, the height of the operationobject may be calculated from a height location (level) of a top surfaceof the operation object and a height location (level) of a bottomsurface of the operation object measured in a state of being gripped bythe end effector.

[16] In addition, according to the present disclosure, there is provideda controller of a robotic system that includes a robot having a roboticarm and an end effector, the controller executing the control methodaccording to any one of [1] to [11].

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is an illustration of an exemplary environment in which a roboticsystem in accordance with an embodiment of the present disclosure mayoperate.

FIG. 2 is a block diagram illustrating an example of a hardwareconfiguration of the robotic system in accordance with the embodiment ofthe present disclosure.

FIG. 3A is a perspective view schematically illustrating a first pose ofan operation object.

FIG. 3B is a perspective view schematically illustrating a second poseof the operation object.

FIG. 3C is a perspective view schematically illustrating a third pose ofthe operation object.

FIG. 4A is a top view illustrating an example task executed by therobotic system in accordance with the embodiment of the presentdisclosure.

FIG. 4B is a front view illustrating an example task executed by therobotic system in accordance with the embodiment of the presentdisclosure.

FIG. 5A is a flow diagram illustrating an example process flow of therobotic system in accordance with the embodiment of the presentdisclosure.

FIG. 5B is a flow diagram illustrating an example process flow of therobotic system in accordance with the embodiment of the presentdisclosure.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

According to the present disclosure, a robotic system in which multipleunits (e.g., various robots, various devices, a controller providedintegrally therewith or separately therefrom, and the like) are highlyintegrated, the controller thereof, a distribution system provided withthese, a method therefor, and the like are provided. For example, arobotic system in accordance with an embodiment of the presentdisclosure is an integrated system that can autonomously execute one ormore tasks. The robotic system in accordance with the embodiment of thepresent disclosure can perform advanced handling of objects that can andsignificantly increase an storage efficiency of a storage container,based on a shape or dimension of an operation object and a space volumeof the storage container, when the operation object is stored in thestorage container and the like. In addition, an advanced scan can beperformed on the operation object by creating or deriving a controlsequence based on a confidence measure related to an initial pose of theoperation object and executing the control sequence.

The robotic system in accordance with the embodiment of the presentdisclosure can be configured to execute a task based on operating (e.g.,physical movement and/or orientation) on the operation object. Morespecifically, for example, the robotic system can sort or transfervarious operation objects by picking up the operation object from apick-up area including a start location (e.g., a large box, a bin, acontainer, a pallet, a storage container, a bucket, a cage, a beltconveyor, and the like as a supply source of the operation object) andmoving the operation object to a placement area including an objectivetask location (e.g., a large box, a bin, a container, a pallet, astorage container, a bucket, a cage, a belt conveyor, and the like as atransfer destination of the operation object).

A control sequence which is executed by the robotic system can includescanning one or more identifiers (e.g., a barcode, a Quick Response (QR)code (registered trademark), and the like) located on one or morespecific locations and/or surfaces of the operation object, duringtransfer. Therefore, the robotic system can execute various tasks suchas gripping and picking up the operation object, scanning the identifierat an appropriate location/orientation, adjusting the pose, changing thepose and shifting (releasing grip, and regripping and picking up theoperation object), transferring the operation object to the tasklocation and releasing grip, and disposing the operation object at thetask location.

The robotic system can further include an imaging device (e.g., acamera, an infrared sensor/camera, a radar, a lidar, and the like) usedto identify a location and a pose of the operation object and anenvironment around the operation object. Further, the robotic system cancalculate a confidence measure associated with the pose of the operationobject. In addition, the robotic system can derive an image indicating alocation and an orientation of the operation object at a time of beingtransferred to a pick-up area including a start location, a placementarea including a task location, an area including a shift location inthe middle of a movement path of the operation object (e.g., a tasktable such as a temporary placement table, other robots, and the like),and the like.

The robotic system can further perform image processing in order toidentify or select an operation object according to a predeterminedorder (e.g., from top to bottom, outside to inside, inside to outside,and the like). Furthermore, for example, the robotic system candetermine the initial pose of the operation object in a pick-up areafrom the image by identifying outlines of the operation object andgrouping the outlines based on a color, a brightness, and adepth/location of a pixel, and/or a combination thereof in a patternimage of imaging data, and changes in their values, for example. Indetermining the initial pose, the robotic system can calculate theconfidence measure according to a predetermined process and/or equation.

The robotic system can further perform shifting of the operation object(changing of the grip location of the operation object) as necessary ata shift location provided in the middle of a route from the pick-up areaincluding the start location and the like to the placement areaincluding the task location and the like. Then, while the operationobject is moved from the pick-up area including the start location andthe like to the placement area including the task location and the like,the robotic system can derive a height of the operation object asnecessary by an imaging device having a distance measuring function, forexample.

The robotic system can further execute a control sequence for executingeach task according to a location, a pose, a height, and a confidencemeasure of the operation object, or a combination thereof, and/or alocation and a pose of the robot, or a combination thereof. Such acontrol sequence can be created or derived by machine learning such asmotion planning and deep learning. The control sequence corresponds togripping of the operation object, manipulating of the operation object,placing the operation object at an objective task location, and thelike, at the start location and/or an any location during movement, inorder to sort, shift, and replace the operation object, for example.

Here, traditional robotic systems execute a control sequence in which anoperation object is gripped in a pick-up area including a start locationand the like, and the operation object is moved to a placement area,including a task location and the like, in the originally gripped state.Therefore, in the traditional systems, the gripped operation object ismerely moved in a gripped state and released from the gripped state, andthus it could not be said that a space in which the operation object isstacked or stored can be used in a sufficiently effective manner. Forthis reason, from a viewpoint of stacking or a storage efficiency ofoperation objects, human intervention (adjustment, re-execution,complementation, system stop, and the like) and an operation inputtherefor may be required.

Unlike the traditional systems, the robotic system according to thepresent disclosure can create or derive a control sequence based onshape information of the operation object and stacking or storageinformation of the operation object and execute the control sequence. Inother words, the robotic system according to the present disclosure canfurther optimize a stacking or storage efficiency of the operationobject based on shape information of the operation object and stackingor storage information of the operation object. In addition, the roboticsystem according to the present disclosure can change a grip location ofthe operation object to a grip location suitable for optimization of astacking or storage efficiency of the operation object, at a shiftlocation that is in the middle of a route between the task location andthe start location.

In addition, unlike the traditional systems, the robotic systemaccording to the present disclosure can create or derive a controlsequence suitable for optimization of a stacking or storage efficiencyof the operation object according to an actual height of the operationobject, as necessary and execute the control sequence. For example, eventhough one or more identifiers located on one or more specific locationsand/or a surface of the scanned operation object are the same, theoperation object may have different shape dimensions. Therefore, on anupstream side (previous stage) of the shift location, in the controlsequence, a height of the operation object is measured based on distanceinformation from the imaging device (camera or distance measuringdevice) located and oriented in along a vertical direction to theoperation object for which a supported location is known, for example.Then, based on the measured height of the operation object, a stackingor storage efficiency of the operation object at the task location canbe calculated. Based on the result, the control sequence can be furtheroptimized.

Further, unlike the traditional systems, the robotic system according tothe present disclosure can create, derive, and implement a controlsequence according to a confidence measure as necessary. For example,according to the confidence measure, approach to the operation objectcan be changed, the grip location on the operation object can bechanged, the pose/location of the operation object can be changed,and/or a part of the movement path can be changed.

In the pose of the operation object gripped in the pick-up area and thelike, generally, a top surface of the operation object can be exposedfacing horizontally (upward) and a side surface of the operation objectcan be exposed facing vertically (laterally). Therefore, the roboticsystem according to the present disclosure can include master data wherethe operation object has one identifier on a bottom surface of theoperation object (i.e., a side opposite to the top surface of theoperation object), and has another identifier on one of the sidesurfaces of the operation object.

The robotic system according to the present disclosure can furthercalculate a confidence measure as necessary when processing an image ofthe pick-up area in identifying an operation object. In a case where theconfidence measure exceeds a sufficiency threshold and there issufficient certainty that the top surface of the operation object isexposed, the robotic system can dispose an end effector on the exposedtop surface, grip the top surface, and rotate the operating object so asto present the bottom surface of the operation object at a predeterminedlocation in front of the scanner. On the other hand, in a case where theconfidence measure is less than a sufficiency threshold and it isuncertain whether the top surface or the bottom surface of the operationobject is exposed, the robotic system can dispose the end effector alongone of the side surfaces of the operation object, grip the side surfaceof the operation object, and rotate the operation object so as to passbetween a set of opposed scanners, for example.

In this case, a task efficiency and a task speed are improved byscanning the operation object in the movement path of the operationobject, for example, between the pick-up area including the startlocation and the placement area including the task location. At thistime, the robotic system according to the present disclosure caneffectively combine a movement task of the operation object and the scantask of the operation object by creating or deriving a control sequencethat also coordinates with or operates the scanner when the operationobject is at the scan location. Further, by creating or deriving acontrol sequence based on the confidence measure of the initial pose ofthe operation object, an efficiency, a speed, and an accuracy regardingthe scan task can be further improved.

The robotic system according to the present disclosure can furthercreate or derive a control sequence corresponding to a case where theinitial pose of the operation object is not accurate. As a result, evenwhen there is an error in determining the pose of the operation object(e.g., an error in determining the result of a calibration error, anunexpected pose, an unexpected light condition, and the like), alikelihood of accurately and reliably scanning the operation object canbe increased. As a result, overall throughput for the robotic system canbe increased and operator efforts/interventions can be further reduced.

Further, in this specification, numerous specific details are set forthto provide a thorough understanding of the present disclosure, but thepresent disclosure is not limited thereto. In addition, in theembodiment of the present disclosure, the techniques described hereincan be implemented without these specific details. Further, well-knownspecific functions, routines, or the like are not described in detail inorder to avoid unnecessarily obscuring the present disclosure.References in this specification to “an embodiment,” “one embodiment,”or the like mean that a particular feature, structure, material, orcharacteristic being described is included in at least one embodiment ofthe present disclosure. Thus, the appearances of such phrases in thisspecification do not necessarily all refer to the same embodiment. Onthe other hand, such references are not necessarily mutually exclusiveeither. Further, the particular features, structures, materials, orcharacteristics can be combined in any suitable manner in one or moreembodiments. In addition, it is to be understood that the variousembodiments shown in the figures are merely illustrative representationsand are not necessarily drawn to scale.

In addition, for structures or processes that are well-known and oftenassociated with robotic systems and subsystems, but that canunnecessarily obscure some significant aspects of the presentdisclosure, the description is omitted for purposes of clarity of thepresent disclosure. Moreover, in this specification, although variousembodiments of the present disclosure are set forth, the presentdisclosure includes configurations or components different fromdescription in this section, as other embodiments. Accordingly, thepresent disclosure can include other embodiments with additionalelements or without some of elements described below.

In addition, each embodiment of the present disclosure can take a formof computer- or controller-executable instructions, including routinesexecuted by a programmable computer or controller. It should be notedthat one of ordinary skill in the art to which the present disclosurebelongs can understand that the techniques of the present disclosure canbe implemented in systems including various computers or controllers.The techniques of the present disclosure can be implemented in a specialpurpose computer or data processor that is programmed, configured, orconstructed to execute one or more instructions on various computers.Accordingly, the terms “computer” and “controller” used herein may beany data processor and can include Internet-based devices and handhelddevices (including palm-top computers, wearable computers, cellular ormobile phones, multi-processor systems, processor-based or programmableconsumer electronics, network computers, mini computers, and the like).Information handled by these computers and controllers can be providedat any suitable display medium such as a liquid crystal display (LCD).Instructions for executing computer- or controller-executable tasks canbe stored in or on any suitable computer-readable medium, includinghardware, firmware, or a combination of hardware and firmware. Inaddition, these instructions can be recorded in any suitable memorydevice including a flash drive and/or other suitable media, for example.

In addition, in this specification, the terms “coupled” and “connected,”along with their derivatives, can be used to describe structuralrelationships between components. It should be understood that theseterms are not intended as synonyms for each other. Rather, in particularembodiments, “connected” can be used to indicate that two or moreelements are in direct contact with each other. Unless otherwise madeapparent in the context, the term “coupled” can be used to indicate thattwo or more elements are in direct contact with each other or indirectcontact with each other with other intervening elements therebetween, orthat the two or more elements cooperate or interact with each other, forexample, as in a cause-and-effect relationship, such as for signaltransmission/reception or for function calls, or both.

Suitable Environments

FIG. 1 is a view illustrating an example environment in which a roboticsystem 100 in accordance with an embodiment of the present disclosurecan operate. The robotic system 100 includes units such as one or morerobots configured to execute one or more tasks.

For the example illustrated in FIG. 1, the robotic system 100 caninclude an unloading unit 102, a transfer unit 104, a transport unit106, a loading unit 108, or a combination thereof in a warehouse or adistribution/transport hub. The various units can be examples of therobots that manipulates an operation object. The robotic units caninclude a robot for operating the operation object by a robotic arm andan end effector, such as a devanning robot, a piece picking robot, and afetching robot. In addition, each unit in the robotic system 100 canexecute a control sequence in which multiple actions are combined so asto perform one or more tasks, for example, the multiple actions such asunloading the operation object from a truck, a van, and the like forstorage in a warehouse, unloading the operation object from a storagelocation, for example, moving the operation object between containers,or loading the operation object into a truck or a van for transport. Inother words, the “task” can include various movements and actionsintended to transfer the operation object from one location” to anotherlocation.

The task can include operating transferring an operation object 112 froma start location 114 of the operation object 112 to a task location 116(e.g., movement, orientation, pose change, and the like), shifting theoperation object 112 at a shift location 118 provided in the middle ofthe movement path of the operation object 112 from the start location114 to the task location 116, scanning the operation object 112 forderiving identification information of the operation object 112, and thelike.

For example, the unloading unit 102 can be configured to transfer theoperation object 112 from a location in a carrier (e.g., a truck) to alocation on a belt conveyor. Further, the transfer unit 104 can beconfigured to transfer the operation object 112 from a location (e.g., apick-up area including a start location) to another location (e.g., aplacement area including a task location on the transport unit 106), andto shift the operation object 112 in the middle of the movement paththereof. Further, the transport unit 106 can transfer the operationobject 112 from an area associated with the transfer unit 104 to an areaassociated with the loading unit 108. Furthermore, the loading unit 108can transfer the operation object 112 from the transfer unit 104 to astorage location (e.g., a predetermined location on a shelf such as arack in a warehouse) by moving, for example, a pallet on which theoperation object 112 is placed.

Further, in the description herein, the robotic system 100 is describedas an example applied in a transport center; however, it is understoodthat the robotic system 100 can be configured to execute tasks in otherenvironments/for other purposes, such as for manufacturing, assembly,packaging, healthcare, and/or other types of automation. It is alsounderstood that the robotic system 100 can include other units, such asa manipulator, a service robot, and a modular robot, which are not shownin FIG. 1. For example, the robotic system 100 can include, an unloadingunit from a pallet to transfer the operation object 112 from a cage cartor a pallet to a conveyor or other pallet, a container-switching unitfor transferring the operation object 112 between containers, apackaging unit for wrapping the operation object 112, a sorting unit forperforming grouping according to characteristics of the operation object112, a picking unit for various operation (e.g., sorting, groupingand/or transferring) the operation object 112 according to thecharacteristics of the operation object 112, a self-propelled carriageunit (e.g., automated guided vehicle, unmanned guided vehicle, and thelike) for moving a pallet or rack for storing the operation object 112,or any combination thereof.

Suitable System

FIG. 2 is a block diagram illustrating an example of a hardwareconfiguration of the robotic system 100 in accordance with theembodiment of the present disclosure. For example, the robotic system100 can include an electronic or electrical device, such as one or moreprocessors 202, one or more storage devices 204, one or morecommunication devices 206, one or more input-output devices 208, one ormore actuation devices 212, one or more transport motors 214, one ormore sensors 216, or a combination thereof. These various electronic orelectrical devices can be coupled to each other via a wire connectionand/or a wireless connection.

The robotic system 100 can include, for example, a bus, such as a systembus, a Peripheral Component Interconnect (PCI) bus or PCI-Express bus, aHyperTransport or industry standard architecture (ISA) bus, a smallcomputer system interface (SCSI) bus, a universal serial bus (USB), anIIC (I2C) bus, or an Institute of Electrical and Electronics Engineers(IEEE) standard 1394 bus (also referred to as “Firewire”). Further, therobotic system 100 can include, for example, a bridge, an adapter, anamplifier, or other signal-related devices for providing the wireconnection between the electronic or electrical devices. In addition,the wireless connection can be based on, for example, a cellularcommunication protocol (e.g., 3G, 4G, LTE, 5G, and the like), a wirelesslocal area network (LAN) protocol (e.g., wireless fidelity (WIFI)), apeer-to-peer or device-to-device communication protocol (e.g., Bluetooth(registered trademark), Near-Field communication (NFC), and the like),an Internet of Things (IoT) protocol (e.g., NB-IoT, LTE-M, and thelike), and/or other wireless communication protocols.

The processor 202 can include a data processor (e.g., a centralprocessing unit (CPU), a special-purpose computer, and/or an onboardserver) configured to execute instructions (e.g., software instructions)stored on the storage device 204 (e.g., a computer memory). Theprocessor 202 can implement the program instructions to control/interactwith other devices, thereby causing the robotic system 100 to execute acontrol sequence including various actions, tasks, and/or operations.

The storage device 204 can include a non-transitory computer-readablemedium having stored thereon program instructions (e.g., software).Examples of the storage device 204 can include, for example, volatilememory (e.g., cache and/or random-access memory (RAM)) and/ornon-volatile memory (e.g., a flash memory and/or a magnetic disk drive),a portable memory drive, and/or a cloud storage device, and the like. Inaddition, the storage device 204 can be used to further store andprovide access to a processing result and/or predetermineddata/threshold, and can store, for example, master data 252 thatincludes information related to the operation object 112.

The master data 252 includes information related to the operation object112, such as a dimension, a shape outline, a mass or a weight, alocation of the center of mass, a template related to a pose and anoutline, model data for recognizing different poses, a stock keepingunit (SKU), a color scheme, an image, identification information, alogo, an expected location of the operation object, an expectedmeasurement value by a sensor (e.g., physical quantity related to aforce, a torque, a pressure, a contact measure value), a combinationthereof, or the like.

The storage device 204 can further store, for example, tracking data 254of the operation object 112. The tracking data 254 can include a log ofan operation object to be scanned or manipulated, imaging data (e.g., aphotograph, a point cloud, a live video, and the like) of the operationobject 112 at one or more locations (e.g., an appropriate startlocation, a task location, a shift location, and the like), and alocation and/or a pose of the operation object 112 at one or morelocations thereof.

The communication device 206 can include, for example, a circuit, areceiver, a transmitter, a modulator/demodulator (modem), a signaldetector, a signal encoder/decoder, a connector port, a network card,and the like, configured to communicate with an external or remotedevice via a network. In addition, the communication device 206 can beconfigured to send, receive, and/or process an electrical signalaccording to one or more communication protocols (e.g., the InternetProtocol (IP), a wireless communication protocol, and the like). Therobotic system 100 can use the communication device 206 to exchangeinformation between respective units or exchange information with anexternal system or an external device for the appropriate purposes of,for example, reporting, data gathering, analyzing, troubleshooting, andthe like.

The input-output device 208 is a user interface device configured toinput information and instructions from the operator and to communicateand present information to the operator, and can include, for example,an input device such as a keyboard, a mouse, a touchscreen, amicrophone, a user interface (UI) sensor (e.g., a camera for receivingmotion commands), and a wearable input device, and an output device suchas a display 210, a speaker, a tactile circuit, and a tactile feedbackdevice. In addition, the robotic system 100 can use the input-outputdevice 208 to communicate with the operator in executing an action, atask, an operation, or a combination thereof.

The robotic system 100 can include, for example, a physical orstructural member (e.g., a robotic manipulator, a robotic arm, and thelike, and hereinafter, referred to as “structural member”) connected bya link or a joint in order to execute a control sequence includingdisplacement such as movement or rotation of the operation object 112.Such a physical or structural member, link, or joint, can be configuredto manipulate an end effector (e.g., a gripper, a hand, and the like)configured to execute one or more tasks (e.g., gripping, rotation,welding, assembly, and the like) in the robotic system 100. In addition,the robotic system 100 can include the actuation device 212 (e.g., amotor, an actuator, a wire, an artificial muscle, an electroactivepolymer, and the like) configured to drive or manipulate (e.g., displaceand/or reorient) the structural member about a joint or at a joint, andthe transport motor 214 configured to transfer the units from a locationto another location.

The robotic system 100 can further include the sensor 216 configured toderive information used to implement the task, such as for manipulatingthe structural member and/or for transferring the unit. The sensor 216can include a device configured to detect or measure one or morephysical characteristics of the robotic system 100 (e.g., a state, acondition, a location, and the like of one or more structural members,links, or joints) and/or characteristics of a surrounding environment,for example, an accelerometer, a gyroscope, a force sensor, a straingauge, a tactile sensor, a torque sensor, a location encoder, and thelike.

Further, the sensor 216 can include one or more imaging devices 222(e.g., a visual and/or infrared camera, a 2-dimensional and/or3-dimensional imaging camera, a distance measuring device such as alidar or a radar, and the like) configured to detect the surroundingenvironment. The imaging device 222 can generate a representation of thedetected environment, such as a digital image and/or a point cloud, inorder to obtain visual information for automatic inspection, robotguidance, or other robot applications, for example.

The robotic system 100 can further process the digital image, the pointcloud, distance measurement data, and the like via, e.g., the processors202 to identify the operation object 112 of FIG. 1, the start location114 of FIG. 1, the task location 116 of FIG. 1, the shift location 118between the start location 114 and the task location 116, a pose of theoperation object 112, a confidence measure regarding the pose of theoperation object at the start location 114 and the like, a confidencemeasure regarding a height of the operation object 112, or a combinationthereof.

Further, in order to manipulate the operation object 112, the roboticsystem 100 can identify the operation object 112, the start location 114thereof, the task location 116 thereof, the shift location 118, and thelike by obtaining and analyzing an image of a designated area (e.g., apick-up area such as in a truck or on a belt conveyor, a placement areafor disposing the operation object 112 on the belt conveyor, area forshifting the operation object 112, an area for disposing the operationobject in the container, an area on the pallet for stacking theoperation object 112, and the like) through various units. In addition,the imaging device 222 can include, for example, one or more camerasconfigured to generate an image of a pick-up area, a placement area, anarea for shifting the operation object 112 set therebetween, and thelike.

The imaging device 222 can further include, for example, one or moredistance measuring devices such as lidars or radars configured tomeasure a distance to the operation object 112 supported at apredetermined location, before or upstream from the shift location 118.Based on the processed image and/or distance measurement data, therobotic system 100 can determine a start location 114, a task location116, a shift location 118, a related pose, an actual height of theoperation object 112, a confidence measure, and the like.

In addition, for scanning the operation object 112, the imaging device222 can include one or more scanners 412 and 416 (e.g., a barcodescanner, a QR code scanner (registered trademark), and the like: seeFIGS. 4A and 4B described below) configured to scan identificationinformation (e.g., an identifier 332 of FIG. 3A and/or FIG. 3C describedbelow) of the operation object 112 during the transport or movement ofthe operation object, for example, between the start location 114 andthe task location 116 (preferably, before the shift location 118).Therefore, the robotic system 100 can create or derive a controlsequence for providing one or more portions of the operation object 112to one or more scanners 412.

The sensor 216 can further include, for example, a location sensor 224(e.g., a location encoder, a potentiometer, and the like) configured todetect a location of a structural member, a link, or a joint. Thislocation sensor 224 can be used to track the location and/or orientationof the structural member, the link, or the joint during execution of thetask.

Further, the sensor 216 can include, for example, a contact sensor 226(e.g., a pressure sensor, a force sensor, a strain gauge, apiezoresistive/piezoelectric sensor, a capacitive sensor, anelastoresistive sensor, other tactile sensors, and the like) configuredto measure a characteristic associated with a direct contact betweenphysical structures or surfaces. The contact sensor 226 can measure thecharacteristic of the operation object 112 corresponding to a grip ofthe end effector. Accordingly, the contact sensor 226 can output acontact measure that represents a quantified and measured value (e.g., ameasured force, torque, location, and the like) corresponding to adegree of contact between the end effector and the operation object 112.Here, the “contact measure” can include, for example, one or more forceor torque readings associated with forces applied to the operationobject 112 by the end effector.

Determination of Confidence Measure for Initial Pose

FIGS. 3A, 3B, and 3C are perspective views schematically illustrating afirst pose 312, a second pose 314, and a third pose 316, respectively,as an example of an operation object 302 in various poses (locations andorientations). In order to identify the pose of the operation object302, the robotic system 100 can process, for example, a 2-dimensionalimage, a 3-dimensional image, a point cloud, and/or other imaging datafrom the imaging device 222. In addition, in order to identify aninitial pose of the operation object 302, the robotic system 100 cananalyze, for example, imaging data by one or more imaging devices 222directed to the pick-up area.

In order to identify the pose of the operation object 302, the roboticsystem 100 can first analyze and identify the operation object 302depicted in the imaging data based on a predetermined recognitionmechanism, a recognition rule, and/or a template related to a pose or anoutline. The robotic system 100 can identify an outline (e.g., aperimeter edge or surface) of the operation object 302, or group theoutlines in the image data. The robotic system 100 can identify, forexample, the groupings of the outlines that correspond to a set ofpixels in the image data that match or correspond to values) the color,the brightness, the depth/location, and/or a combination thereof of acorresponding aspect of an object registered in the master data 252.

When the outlines of the operation object 302 are grouped, the roboticsystem 100 can identify, for example, one or more surfaces, edges,and/or points, and poses of the operation object 302 in a grid orcoordinate system used in the robotic system 100.

In addition, the robotic system 100 can identify one or more exposedsurfaces (e.g., a first exposed surface 304, a second exposed surface306, and the like) of the operation object 302 in the imaging data.Further, the robotic system 100 can identify the operation object 302,for example, by determining an outline shape and one or more dimensions(e.g., a length, a width, and/or a height) of the operation object 302from the imaging data according to the outline and the calibration ofthe operation object 302 or mapping data for the imaging device 222, andcomparing the determined dimensions with corresponding data in themaster data 252. Further, the robotic system 100 can identify whether anexposed surface corresponds to any of a top surface 322, a bottomsurface 324, and an outer peripheral surface 326, based on a length, awidth, and a height of the operation object 302 in which dimensions ofthe exposed surface is identified.

In addition, the robotic system 100 can identify the operation object302, for example, by comparing one or more markings (e.g., a letter, anumber, a shape, a visual image, a logo, or a combination thereof)displayed on one or more exposed surfaces with one or more predeterminedimages in the master data 252. In this case, the master data 252 caninclude, for example, one or more images of a product name, a logo, adesign/image on a package surface of the operation object 302, or acombination thereof. In addition, the robotic system 100 can identifythe operation object 302 by comparing a portion of the imaging data(e.g., a portion within an outline of the operation object 302) with themaster data 252, and similarly, can identify a pose (particularly, anorientation) of the operation object 302 based on a unique andpredetermined image pattern on a surface.

FIG. 3A illustrates a first pose 312 where the first exposed surface 304(e.g., an upward-facing exposed surface) is the top surface 322 of theoperation object 302 and the second exposed surface 306 (e.g., anexposed surface generally facing a source of the imaging data) is one ofthe peripheral surfaces 326 of the operation object 302.

In identifying the exposed surfaces, the robotic system 100 can processthe imaging data of FIG. 3A to map measurement values of the dimensions(e.g., the number of pixels) of the first exposed surface 304 and/or thesecond exposed surface 306 into real-world dimensions using apredetermined calibration or mapping function. The robotic system 100can compare the mapped dimensions with dimensions of the known/expectedoperation object 302 in the master data 252 and identify the operationobject 302 based on the result. Further, since a pair of intersectingedges that define the first exposed surface 304 matches the length andthe width of the identified operation object 302, the robotic system 100can identify that the first exposed surface 304 is either the topsurface 322 or the bottom surface 324. Similarly, because one of theedges defining the second exposed surface 306 matches the height of theidentified operation object 302, the robotic system 100 can identify thesecond exposed surface 306 as the peripheral surface 326.

In addition, the robotic system 100 can process the imaging data of FIG.3A to identify one or more markings unique to a surface of the operationobject 302. In this case, the master data 252 can include one or moreimages and/or other visual characteristics (e.g., a color, a dimension,a size, and the like) of surfaces and/or unique markings of theoperation object 302 as described above. As illustrated in FIG. 3A,since the operation object 302 has “A” on the top surface 322, therobotic system 100 can identify the operation object 302 as a registeredobject stored in the master data 252, and identify the first exposedsurface 304 as the top surface 322 of the operation object 302.

In addition, the master data 252 can include an identifier 332 asidentification information of the operation object 302. Morespecifically, the master data 252 can include an image and/or codedmessage of the identifier 332 of the operation object 302, a location334 of the identifier 332 relative to a surface and/or a set of edges,one or more visual characteristics thereof, or a combination thereof. Asillustrated in FIG. 3A, the robotic system 100 can identify the secondexposed surface 306 as the peripheral surface 326 based on the presenceof the identifier 332 and/or the location thereof matching the location334 of the identifier 332.

FIG. 3B illustrates a second pose 314 obtained by rotating the operationobject 302 by 90 degrees about a vertical axis along a direction B inFIG. 3A. For example, a reference point “a” of the operation object 302can be in a lower left front corner in FIG. 3A and in an upper rightback corner in FIG. 3B. Accordingly, in comparison with the first pose312, the top surface 322 of the operation object 302 can be recognizedas a different orientation in the imaging data and/or the peripheralsurface 326 of the operation object 302 having the identifier 332 maynot be visually recognized.

The robotic system 100 can identify various poses of the operationobject 302 based on a specific orientation of the identifier 332 havingone or more visual features. For example, it is possible to determinethe first pose 312 and/or the third pose 316 in a case where a dimensionmatching a known length of the operation object 302 extends horizontallyin the imaging data, a dimension matching a known height of theoperation object 302 extends vertically in the imaging data, and/or adimension matching a known width of the operation object 302 extendsalong a depth axis in the imaging data. Similarly, the robotic system100 can determine the second pose 314 in a case where a dimensionmatching a width extends horizontally in the imaging data, a dimensionmatching a height extends vertically in the imaging data, and/or adimension matching a length extends along a depth axis in the imagingdata.

In addition, the robotic system 100 can determine that the operationobject 302 is in the first pose 312 or the second pose 314 based on anorientation of a visible marking such as “A” illustrated in FIG. 3A andFIG. 3B, for example. In addition, for example, in a case where theidentifier 332 of the operation object 302 is visually recognized with amarking “A” (i.e., on a different surface), the robotic system 100 candetermine that the operation object 302 is in the first pose 312 basedon the visible marking to be visually recognized in a combination ofrespective surfaces.

FIG. 3C illustrates the third pose 316 obtained by rotating theoperation object 302 by 180 degrees about a horizontal axis along adirection C in FIG. 3A. For example, a reference point “a” of theoperation object 302 can be in a lower left front corner in FIG. 3A andin an upper left back corner in FIG. 3C. Accordingly, in comparison withthe first pose 312, the first exposed surface 304 is the bottom surface324 of the operation object, and both the top surface 322 and theperipheral surface 326 having the identifier 332 of the operation object302 are not visually recognized.

As described above, the robotic system 100 can identify that theoperation object 302 is in either the first pose 312 or the third pose316 based on the dimensions determined from the image data. The roboticsystem 100 can further identify that the operation object 302 is in thefirst pose 312 in a case where the marker (e.g., “A”) of the top surface322 is visible. In addition, the robotic system 100 can identify thatthe operation object 302 is in the third pose 316 in a case where abottom-surface marker (e.g., an instance of the identifier 332 of theoperation object) is visually recognized.

When determining the pose of the operation object 302, real-worldconditions may affect an accuracy of the determination. For example,lighting conditions may reduce visibility of a surface marking due to areflection and/or a shadow. In addition, according to an actualorientation of the operation object 302, an exposure or viewing angle ofone or more surfaces may be reduced, and therefore any marking on thesurface may be unidentifiable. Accordingly, the robotic system 100 cancalculate a confidence measure associated with the determined pose ofthe operation object 302.

The robotic system 100 can further calculate the confidence measurebased on a certainty interval associated with the dimension measurementwithin the image in the imaging data. In this case, the certaintyinterval can increase as a distance between the operation object 302 andan imaging source (e.g., the imaging device 222) decreases and/or in acase where a measured edge of the operation object 302 is closer to theimaging source in a direction orthogonal to a direction radiating fromthe imaging source and farther away from the imaging source in adirection parallel to the radiating direction. Also, the robotic system100 can calculate, for example, the confidence measure based on a degreeof match between a marker or a design in the imaging data and a knownmarker/design in the master data 252. Furthermore, the robotic system100 can measure a degree of an overlap or a deviation between at least aportion of the imaging data and a predetermined marker/image.

In this case, the robotic system 100 can identify the operation object302 and/or an orientation thereof according to a greatest overlap and/ora lowest deviation measurement for a minimum mean square error (MMSE)mechanism, and furthermore can calculate a confidence measure based on adegree of the obtained overlap/deviation. The robotic system 100 cancalculate the movement path of the operation object 302 in the controlsequence based on the obtained confidence measure. In other words, therobotic system 100 can transfer the operation object 302 differentlyaccording to the obtained confidence measure.

System Operation

FIG. 4A is a top view illustrating an example task 402 which is executedby the robotic system 100 in accordance with the embodiment of thepresent disclosure. As described above, the example task 402 cancorrespond to a control sequence which is executed by the robotic system100 (e.g., executed by one of the units illustrated in FIG. 1). Asillustrated in FIG. 4A, for example, the task 402 can include moving theoperation object 112 from the pick-up area including the start location114 to the placement area including the task location 116 via the shiftlocation 118. Also, the task 402 can include scanning the operationobject 112 while moving the operation object from the start location 114to the task location 116, and shifting the operation object 112 at theshift location 118 (by changing the grip location). Accordingly, therobotic system 100 can update the tracking data 254 of the operationobject 112 by adding the scanned operation object 112 to the trackingdata 254, removing the operation object 112 from the tracking data 254,and/or evaluating the operation object 112, and the like.

In addition, in order to identify and/or specify the start location 114,the robotic system 100 can include a scanner 412 (an instance of theimaging device 222) such as a 3D vision device directed at a pick-uparea so as to image the pick-up area (e.g., an area designated for apart procurement pallet or a large box and/or a region on a receivingside of a belt conveyor, and the like), and thereby can derive imagingdata of the designated area. Therefore, the robotic system 100 canimplement a computer aided image process (vision process) for theimaging data, in order to identify the various operation objects 112located in the designated area via the processor 202, for example.

In addition, the robotic system 100 can select an operation object 112for which the task 402 is to be executed from among the recognizedoperation objects 112. The robotic system 100 can select based on, forexample, a predetermined selection measure, and/or a selection rule,and/or a template related to a pose or an outline. The robotic system100 can further process the imaging data in order to determine the startlocation 114 and/or the initial pose for the selected operation object112.

In order to identify and/or specify the task location 116 and the shiftlocation 118, the robotic system 100 can include other scanners 416 (aninstance of the imaging device 222) facing the following areas so as toimage the placement area and other predetermined areas (e.g., an areadesignated for a sorted pallet or a large box and/or a region on areceiving side of a belt conveyor, and the like). Accordingly, therobotic system 100 can derive imaging data of the designated area.Therefore, the robotic system 100 can implement a computer aided imageprocess (vision process) for the imaging data, in order to identify thetask location 116 for disposing the operation object 112, the shiftlocation 118, and/or the pose of the operation object 112 via theprocessor 202, for example. In addition, the robotic system 100 canidentify and select the task location 116 and the shift location 118based on a predetermined criterion or rule for stacking and/or disposingmultiple operation objects 112 (based on the imaging result or not basedon the imaging result).

The scanner 416 can be disposed to face in a horizontal direction so asto scan a mark that is adjacent thereto (e.g., at a height correspondingto a height of the corresponding scanner(s)) and on a verticallyoriented surface of the operation object 112. Further, the scanner 416can be disposed to face in a vertical direction so as to scan a markthat is above/below thereof and on a horizontally oriented surface ofthe operation object 112. Furthermore, the scanners 416 can be disposedto oppose each other so as to scan opposite sides of the operationobject 112 that is placed between the scanners 416.

In addition, the robotic system 100 can operate the operation object 112so as to place the operation object 112 at a presentation locationand/or so as to scan one or more surfaces/portions of the operationobject 112 with the scanners 416, according to the location and/orscanning direction of the scanner 416. Further, the robotic system 100can include the imaging device 222 configured to measure a heightlocation of the bottom surface 324 of the operation object 112 which hasbeen scanned by the scanner 416 and a support location of which isknown, for example (see FIG. 4B).

In order to execute the task 402 using such an identified start location114, the shift location 118, and/or the task location 116, the roboticsystem 100 can operate one or more structural members (e.g., a roboticarm 414 and/or the end effector) of each unit. Accordingly, the roboticsystem 100 can create or derive a control sequence that corresponds toone or more actions that will be implemented by the corresponding unitto execute the task 402, via the processor 202, for example.

For example, the control sequence for the transfer unit 104 can includeplacing the end effector at an approach location (e.g., alocation/position for gripping the operation object 112), gripping theoperation object 112, lifting the operation object 112, moving theoperation object 112 from above the start location 114 to thepresentation location/pose for the scanning operation, shifting theoperation object 112 at the shift location 118 (changing the griplocation), moving the operation object 112 from the start location 114to above the task location 116, as necessary, via a shift location 118,lowering the operation object 112, and releasing the operation object112.

In addition, the robotic system 100 can create or derive the controlsequence by determining a sequence of commands and/or settings for oneor more actuation devices 212 that operate the robotic arm 414 and/orthe end effector. In this case, the robotic system 100 can use, forexample, the processor 202 to calculate the commands and/or settings ofthe actuation device 212 for manipulating the end effector and therobotic arm 414 to place the end effector at the approach location aboutthe start location 114, grip the operation object 112 with the endeffector, place the end effector at the approach location around thescan location or shift location 118, place the end effector at theapproach location around the task location 116, and release theoperation object 112 from the end effector. Accordingly, the roboticsystem 100 can execute an operation for completing the task 402 byoperating the actuation device 212 according to the determined controlsequence of commands and/or settings.

In addition, the robotic system 100 can create or derive a controlsequence based on the confidence measure for the pose of the operationobject 112. In this case, the robotic system 100 can consider placementof the end effector at various locations for pickup in order to grip orcover a different surface, calculate various presentationlocations/poses for the operation object 112, or a combination thereofaccording to the confidence measure for the pose, for example.

As an illustrative example, in a case where the operation object 112 isthe operation object 302 placed in the first pose 312 of FIG. 3A (inthis case, the top surface 322 of the operation object 302 generallyfaces upward and is exposed) and the confidence measure for the pose ishigh (i.e., a degree of a certainty exceeds the sufficiency thresholdand the determined pose is more likely accurate), the robotic system 100can create or derive a first control sequence 422 that includes a firstapproach location 432 and a first presentation location 442. At thistime, for example, since there is a sufficient certainty that the topsurface 322 of the operation object 302 faces upward (i.e., the bottomsurface 324 with the identifier 332 of the operation object 302 of FIG.3C faces downward), the robotic system 100 can calculate the firstcontrol sequence 422 that includes the first approach location 432 forplacing the end effector directly over the top surface 322 of theoperation object 302.

As a result, the robotic system 100 can grip the operation object 112with the end effector contacting/covering the top surface 322 of theoperation object 302 such that the bottom surface 324 of the operationobject 302 is exposed. In addition, the robotic system 100 can calculatethe first control sequence 422 that includes the first presentationlocation 442 for causing the operation object 112 to be directly over anupward-facing scanner 416 for scanning the identifier 332 located on thebottom surface 324.

In contrast, in a case where the confidence measure for the pose is low(i.e., a degree of a certainty is less than a sufficiency threshold anda likelihood that the determined pose is accurate is low), the roboticsystem 100 can create or derive a second control sequence 424 (i.e.,different from the first control sequence 422) that includes a secondapproach location 434 and one or more second presentation locations 444.At this time, for example, the robotic system 100 can measure thedimensions of the operation object 112, compare the dimensions with themaster data 252, and determine that the operation object 302 is ineither the first pose 312 of FIG. 3A or the third pose 316 of FIG. 3C(e.g., in a case where a certainty level of the measurement exceeds apredetermined threshold).

However, the robotic system 100 may have a difficulty inimaging/processing a mark printed on the surface of the operation object112, and as a result, the confidence measure associated with thedetermined pose can be less than a sufficiency threshold. In otherwords, the robotic system 100 may not be sufficiently certain whetherthe upward-facing exposed surface of the operation object 302 is the topsurface 322 thereof (corresponding to, e.g., the first pose 312) or thebottom surface 324 thereof (corresponding to, e.g., the third pose 316).

In this case, due to the low degree of the confidence measure (the lowdegree of a certainty), the robotic system 100 can calculate the secondcontrol sequence 424 that includes the second approach location 434 forplacing the end effector (e.g., aligned with and/or facing in adirection parallel to the top surface 322 and/or the bottom surface 324of the operation object 302) to be adjacent to one of the peripheralsurfaces 326 of the operation object 302 of FIG. 3A.

As a result, the robotic system 100 can grip the operation object 112with the end effector contacting/covering one of the peripheral surfaces326 of the operation object 302 and causing both the top surface 322 andthe bottom surface 324 of the operation object 302 to be exposed. Inaddition, the robotic system 100 can simultaneously or sequentiallypresent or place the top surface 322 and the bottom surface 324 of theoperation object 302 in front of the scanners 416 (e.g., in a scanningfield and/or in a state of facing the scanning field). In a case wherethe operation object 112 is in the scan location, the robotic system 100can operate the scanners 416 (e.g., at least the scanners 416 facing thetop surface 322 and the bottom surface 324 of the operation object 302)to simultaneously and/or sequentially scan the presented surfaces andderive the identifier(s) 332 of the operation object 302 above thescanner.

In addition, the second control sequence 424 includes the secondpresentation location(s) 444 for disposing the surface that facesdownward initially (the bottom surface 324 of the operation object 302)horizontally and directly over the upward-facing scanner 416 and/or forplacing the surface that faces upward initially (the top surface 322 ofthe operation object) vertically and directly in front of ahorizontally-facing scanner 416. The second control sequence 424 caninclude a reorienting/rotating action (e.g., an action as represented bya dotted-unfilled circle) for providing two presentationlocations/poses, and thereby both the top surface 322 and the bottomsurface 324 are scanned using orthogonally facing scanners 416. Further,for example, the robotic system 100 can sequentially present the topsurface 322 of the operation object 302 to the upward-facing scanner andscan the top surface, and then rotate the operation object 302 by 90degrees to present the bottom surface 324 thereof to thehorizontally-facing scanner 416 for scanning. At that time, thereorienting/rotating action can be conditional such that the roboticsystem 100 implements the corresponding commands in a case where readingthe identifier 332 of the operation object 302 fails.

Also, as an example, the robotic system 100 can create or derive acontrol sequence (not shown) for gripping/covering one of the peripheralsurfaces 326 along a width of the operation object 302 in a case wherethe confidence measure is low. The robotic system 100 can move theoperation object 302 between a horizontally opposing pair of thescanners 416 to present the peripheral surfaces 326 of the operationobject 302 along the length thereof and scan the identifier 332 on oneof the peripheral surfaces 326 for example, as illustrated in FIG. 3A,for example. Further, details regarding the control sequence based onthe confidence measure will be described later with reference to FIGS.5A and 5B described later.

In addition, the robotic system 100 can derive the control sequencebased on a 2-dimensional or 3-dimensional shape of the operation object112 gripped by the end effector (hereinafter, referred to as the“operation object 112” instead of the “operation object 302”) and theinformation regarding the operation object 112 in a storage container450 placed at the task location 116 (e.g., a box, a bin, and the like).

As an example, the robotic system 100 determine dimensions of theoperation object 112 in both cases of the first control sequence and thesecond control sequence described above, for example. The robotic system100 can determine/track placement locations, orientations, and/ordimensions of other objects (e.g., previously stored objects) in thestorage container 450 placed at the task location 116. Accordingly, therobotic system 100 can obtain information regarding open/available spacein the storage container 450. The robotic system 100 can calculate spaceshape parameters of the operation object 112 according to various posesof the operation object 112 and according to various different griplocations. Therefore, by comparing these space shape parameters with theavailable space in the storage container 450, the robotic system 100 canoptimize and select a pattern or plan with which the operation object112 can be stored at a higher filling density in the storage container450.

In this case, when the end effector accesses the storage container 450,the robotic system 100 can consider the presence/absence of interferencebetween the end effector and the storage container 450 or the operationobject 112 already stored. Therefore, the robotic system 100 candetermine an increase in a filling rate in the storage container 450 fora pose change or a grip location change. Accordingly, the robotic system100 can create or derive a control sequence including an operation ofshifting the operation object 112 to a pose associated with theincreased/higher storage rate.

FIG. 4B is a front view illustrating an example task 404 which isexecuted by the robotic system 100 in accordance with the embodiment ofthe present disclosure. In this example, multiple operation objects 112are placed on a pallet 464 that is carried to the pick-up area includingthe start location 114 in a state of being mounted on a self-propelledcarriage unit 462 such as an automated guided vehicle (AGV). Forillustrative purposes, FIG. 4B shows multiple operation objects 112having the same shape and stacked according to a pattern However, it isunderstood that, in many cases, multiple operation objects 112 havingdifferent dimensions may be randomly stacked on the pallet 464.

The pick-up area in which the pallet 464 is carried is imaged by thescanner 412, and the operation object 112 is selected in the same manneras described with reference to FIG. 4A. For the selected operationobject 112, in this example, the top surface 322 of the operation object112 is gripped by the end effector installed at the tip of the roboticarm 414 of the transfer unit 104, the identifier 332 is scanned with thescanner 416, and the information of the identifier 332 is derived. Forexample, the robotic system 100 can obtain information including thedimensions of the operation object 112 by comparing the information ofthe identifier 332 of the operation object 112 with the master data 252.

In some instances, objects having the same identifier 332 may actuallyhave different dimensions (particularly height). Therefore, for example,when scanning the operation object 112, the robotic system 100 measuresthe distance to the bottom surface 324 of the operation object 112 byusing a distance measuring device 466 (an example of the imaging device222) installed on a floor of the task space or near the floor surface.At this time, in a case where the movement of the operation object 112is temporarily stopped during scanning, the distance to the bottomsurface 324 of the operation object 112 can be measured during thetemporary stop. For illustrative purposes, FIG. 4B show that themeasurement by the distance measuring device 466 is performedimmediately after the operation object 112 is unloaded (depalletized)from the pallet 464. However, it is understood that a timing of themeasurement is not particularly limited The distance measuring device466 can be configured to obtain the measurements at an upstream locationof (before) the shift location 118 in the control sequence.

In this example, the robotic system 100 can obtain a height location(gripping level) of the top surface 322 of the operation object 112 atthe time of measurement according to a control sequence or anappropriate location measurement. Therefore, a height 112 h of theoperation object 112 can be derived using the height level and themeasured value of the distance to the bottom surface 324 of theoperation object 112. That is, the robotic system 100 receives themeasurement data of the bottom surface 324 of the operation object 112by the distance measuring device 466, and the height 112 h can becalculated from the received measurement data and the height location(gripping level) of the top surface 322 of the operation object 112. Ina case where the height 112 h is different from a value stored as themaster data 252 of the operation object 112, the robotic system 100 canreplace the master data 252 or update the master data 252 by adding thedifferent value thereto.

After actual dimensions of the operation object 112 are determined inthis way, the robotic system 100 can calculate the space shapeparameters of the pose when the operation object 112 is gripped invarious directions. Therefore, by comparing these space shape parameterswith the information on a space in the storage container 450 placed atthe task location 116, the robotic system 100 can optimize and select aplan or pattern with which the operation object 112 is stored at ahigher filling density in the storage container 450.

At this time, when the end effector accesses the storage container 450,the robotic system 100 calculates the presence/absence of interferencebetween the end effector and the storage container 450 or thealready-stored objects. When the interference may occur, the pattern canbe eliminated. Therefore, when a new pose or grip location differentthan the current grip location/orientation increases a filling rate ofthe storage container 450, the robotic system 100 can change remainingportions of the control sequence 472 (corresponding to the first controlsequence 422 or the second control sequence 424 in FIGS. 4A and 4B) andcreate an updated control sequence including an operation of shiftingthe operation object 112 so as to be a pose optimized for storage.

On the other hand, in a case where the pose of the operation object 112gripped at a current time point is optimal from a viewpoint of a storageefficiency, the robotic system 100 stores the gripped operation object112 in the storage container 450 without changing the control sequence472.

In addition, in a case where the operation object 112 is to be shifted,the robotic system 100 operates the operation object 112 according tothe updated control sequence 474. For example, the operation object 112can be moved to a peripheral area of the shift location 118 after thescan. The robotic system 100 can orient the end effector according to apredetermined orientation for temporary placement, place the operationobject 112 on a temporary placing table 468 accordingly, and release thegrip. The temporary placing table is not particularly limited and caninclude, for example, a pedestal and the like which can place theoperation object 112 so that at least two surfaces thereof are exposed.In some embodiments, the temporary placing table may be configured tohold the operation object 112 in a tilted state while supporting theoperation object 112, thereby improving access to grip the object andincrease stability during the gripping operation. The robotic system 100can shift the operation object 112 by changing the orientation of theend effector and gripping surfaces of the operation object 112 differentfrom the previous grip locations before temporarily placing theoperation object 112.

The robotic system 100 stores the shifted operation object 112 in thestorage container 450. At this time, the end effector may be rotated oradjusted with respect to a target location without directly positioningthe end effector at a time. In addition, multiple end effectors ormultiple units may be provided, and control may be performed so thateach end effector is properly used in relation to the size of theoperation object 112.

Further, in the above description, in order to execute the actions forthe task 402, the robotic system 100 can track a current location (e.g.,a set of coordinates corresponding to a coordinate system used by therobotic system 100) and/or a current pose of the operation object 112.For example, the robotic system 100 can track the current location/poseaccording to data from the location sensor 224 of FIG. 2 via theprocessor 202, for example. The robotic system 100 can place one or moreportions of the robotic arm 414 (e.g., the link or the joint) accordingto data from the location sensor 224. The robotic system 100 can furthercalculate the location/pose of the end effector, and thereby calculatethe current location of the operation object 112 held by the endeffector, based on the location and orientation of the robotic arm 414.Also, the robotic system 100 can track the current location based onprocessing other sensor readings (e.g., force readings or accelerometerreadings), the executed actuation commands/settings, and/or theassociated timings, or a combination thereof according to adead-reckoning mechanism.

Operational Flow (Control Sequence Based on Confidence Measure)

FIG. 5A is a flow diagram of a method 500 illustrating an exampleprocess flow of the robotic system 100 in accordance with the embodimentof the present disclosure. The method 500 includes a procedure ofderiving/calculating and implementing a control sequence based on aconfidence measure to execute the task 402 of FIG. 4A. The confidencemeasure can be associated with determining the initial pose of theoperation object 112. In addition, the method 500 can be implementedbased on executing the instructions stored on one or more storagedevices 204 with one or more processors 202.

At block 501, the robotic system 100 can identify scanning fields of oneor more imaging devices 222 of FIG. 2. For example, the robotic system100 (via, e.g., one or more processors 202) can identify spaces that canbe scanned by one or more imaging devices 222, such as the scanners 412and 416 of FIGS. 4A and 4B. The robotic system 100 can identify thescanning fields that are oriented in opposite directions and/ororthogonal directions according to orientations of the scanners 416. Asillustrated in FIGS. 4A and 4B, the scanners 416 can be arrangedopposite to each other and/or facing each other, such as across ahorizontal direction or across a vertical direction. Also, the scanners416 can be arranged perpendicular to each other, such as one facing upor down and another facing a horizontal direction.

For example, the robotic system 100 can identify the scanning fieldsaccording to the master data 252. The master data 252 can include gridlocations, coordinates, and/or other markers representing the imagingdevices 222 and/or the corresponding scanning fields. The master data252 can be predetermined according to a layout and/or a physicaldislocation of the imaging devices 222, the capabilities of the imagingdevice 222, environmental factors (e.g., lighting conditions and/orobstacles/structures), or a combination thereof. In addition, therobotic system 100 can implement a calibration process to identify thescanning fields. For example, the robotic system 100 can use thetransfer unit 104 to place a known mark or code at a set of locationsand determine whether the corresponding imaging device 222 accuratelyscans the known mark. The robotic system 100 can identify the scanningfields based on the locations of the known mark that resulted inaccurate scanning results.

At block 502, the robotic system 100 can scan designated areas. Therobotic system 100 can generate (via, e.g., via a command/prompt sent bythe processor 202) imaging data (e.g., the derived digital images and/orpoint clouds) of one or more designated areas, such as the pick-up areaand/or the placement area, using one or more imaging devices 222 (e.g.,the scanners 412 of FIGS. 4A and 4B and/or other area scanners). Theimaging data can be communicated from the imaging devices 222 to the oneor more processors 202. Accordingly, one or more processors 202 canreceive the imaging data that represents the pick-up area (including,e.g., operation objects 112 before execution of the task), the shiftarea, and/or the placement area (including, e.g., operation objects 112after execution of the task) for further processing.

At block 504, the robotic system 100 can identify the operation object112, the associated locations (e.g., the start location 114 of FIG. 1and/or the task location 116 of FIG. 1), and/or the initial poses of theoperation objects 112. The robotic system 100 can analyze (via, e.g.,the processor 202) the imaging data based on a pattern recognitionmechanism and/or a recognition rule in order to identify outlines (e.g.,perimeter edges and/or surfaces) of the operation objects 112. Therobotic system 100 can further identify the groupings of outlines and/orsurfaces of the operation objects 112 based on, for example, apredetermined recognition mechanism, a recognition rule, and/ortemplates related to poses or outlines as corresponding to the variousoperation objects 112.

For example, the robotic system 100 can identify the groupings of theoutlines of the operation objects 112 that correspond to a pattern(having, e.g., the same values or values that vary at a knownrate/pattern) in the color, the brightness, the depth/location, and/or acombination thereof over the outlines of the operation objects 112. Inaddition, for example, the robotic system 100 can identify the groupingsof the outlines and/or surfaces of the operation objects 112 accordingto predetermined shape/pose templates, images, or a combination thereofdefined in the master data 252.

From the operation objects 112 recognized in the pick-up area, therobotic system 100 can select one as the operation object 112 (e.g.,according to a predetermined sequence or set of rules and/or templatesof outlines of operation objects). For example, the robotic system 100can select the operation object 112 according to the point cloudrepresenting the distances/locations relative to a known location of thescanner 412. In addition, for example, the robotic system 100 can selectthe operation object 112 that is located at a corner/edge and has two ormore surfaces that are exposed/shown in the imaging results. Further,the robotic system 100 can select the operation object 112 according toa predetermined pattern or sequence (e.g., left to right, nearest tofarthest, and the like, relative to a reference location).

For the selected operation object 112, the robotic system 100 canfurther process the imaging data in order to determine the startlocation 114 and/or an initial pose. For example, the robotic system 100can determine the start location 114 by mapping a location (e.g., apredetermined reference point for the determined pose) of the operationobject 112 in the imaging data to a location in the grid used by therobotic system 100. The robotic system 100 can map the locationsaccording to a predetermined calibration map.

The robotic system 100 can process the imaging data of the placementareas to determine available/open spaces between already-placed objects.The robotic system 100 can map the outlines of the operation object 112according to a predetermined calibration map for mapping image locationsto real-world locations and/or coordinates used by the system. Based onthe mapping, the robotic system 100 can determine the open spaces. Therobotic system 100 can determine the open spaces as the space betweenthe outlines (furthermore, surfaces of the operation object 112)belonging to different groupings that each correspond to analready-placed object. The robotic system 100 can determine the openspaces suitable for the operation object 112 by measuring one or moredimensions of the open spaces and comparing the measured dimensions withone or more dimensions of the operation objects 112 (e.g., as stored inthe master data 252). In addition, the robotic system 100 can select oneof the suitable/open spaces as the task location 116 according to apredetermined pattern (e.g., left to right, nearest to farthest, bottomto top, and the like, relative to a reference location).

The robotic system 100 can determine the task location 116 withoutprocessing the imaging data or in addition to processing the imagingdata. For example, the robotic system 100 can place a set of objects atthe placement area according to a predetermined control sequence andlocations without re-imaging the area after each placement. Also, forexample, the robotic system 100 can process the imaging data forperforming multiple tasks (e.g., moving multiple operation objects 112,such as tasks for operation objects 112 located on a common layer/columnof a stack).

At block 522, for example, the robotic system 100 can determine aninitial pose (e.g., an estimate of a stopped pose of the operationobject 112 at the pick-up area) based on processing the imaging data(e.g., the imaging data from the scanner 412). The robotic system 100can determine the initial pose of the operation object 112 based oncomparing the outline of the operation object 112 with outlines inpredetermined pose templates of the master data 252 (e.g., comparingpixel values). For example, the templates of the predetermined pose caninclude a different potential arrangement of the outlines of theoperation objects 112 according to corresponding orientations ofexpected operation objects 112. The robotic system 100 can identify thesets of outlines of the operation objects 112 (e.g., edges of an exposedsurface, such as the first exposed surface 304 of FIG. 3A and/or FIG. 3Cand/or the second exposed surface 306 of FIG. 3A) that were previouslyassociated with the selected operation object 112. The robotic system100 can determine the initial pose by selecting one of the posetemplates that corresponds to a lowest difference measurement betweenthe compared outlines of the operation objects 112.

For further example, the robotic system 100 can determine the initialpose of the operation object 112 based on physical dimensions of theoperation object 112. The robotic system 100 can estimate physicaldimensions of the operation object 112 based on the dimensions of theexposed surfaces captured in the imaging data. The robotic system 100can measure a length and/or an angle for each outline of the operationobject 112 in the imaging data and then map or transform the measuredlength to a real-world length or a standard length using a calibrationmap, a transformation table or process, a predetermined equation, or acombination thereof. The robotic system 100 can use the measureddimensions to identify the operation object 112 and/or the exposedsurface(s) corresponding to the physical dimensions.

The robotic system 100 can identify the operation object 112 and/or theexposed surface(s) by comparing the estimated physical dimensions with aset of known dimensions (e.g., a height, a length, and/or a width) ofthe operation objects 112 and their surfaces in the master data 252. Therobotic system 100 can identify the exposed surface(s) and thecorresponding pose using the matched set of dimensions. For example, therobotic system 100 can identify the exposed surface as either the topsurface 322 of the operation object 302 of FIG. 3A or the bottom surface324 of the operation object 302 of FIG. 3B (e.g., a pair of opposingsurfaces) in a case where the dimensions of the exposed surface match alength and a width for an expected operation object 112. Based on theorientation of the exposed surface, the robotic system 100 can determinethe initial pose of the operation object 112 (e.g., either the firstpose 312 or the third pose 316 of the operation object 302 in a casewhere the exposed surface faces upward).

For example, the robotic system 100 can determine the initial pose ofthe operation object 112 based on a visual image of one or more surfacesof the operation object 112 and/or one or more markings thereof. Therobotic system 100 can compare the pixel values within a set ofconnected outlines with predetermined marking-based pose templates ofthe master data 252. For example, the marking-based pose templates caninclude one or more unique markings of expected operation objects 112 invarious different orientations. The robotic system 100 can determine theinitial pose of the operation object 112 by selecting one of thesurfaces, the surface orientations, and/or the corresponding poses thatresult in a lowest difference measurement for the compared images.

At block 524, the robotic system 100 can calculate a confidence measureassociated with the initial pose of the operation object 112. Therobotic system 100 can calculate the confidence measure as a part ofdetermining the initial pose. For example, the confidence measure cancorrespond to a measure of a difference between the outline of theoperation object 112 and the outline in the selected template describedabove. In addition, for example, the confidence measure can correspondto a tolerance level associated with the estimated physical dimensionsand/or the angles described above. Also, for example, the confidencemeasure can correspond to the difference measure between a visualmarking in the imaging data and the template images described above.

At block 506, the robotic system 100 can calculate a control sequencefor executing the task 402 related to the operation object 112 (e.g.,the first control sequence 422 of FIG. 4A, the second control sequence424 of FIG. 4A, the control sequence 472 in FIG. 4B, and the like), andthe control sequence 474 including a shift operation of the operationobject 112 illustrated in FIG. 4B.

For example, the robotic system 100 can create or derive the controlsequence based on calculating a sequence of commands or settings, or acombination thereof, for the actuation devices 212 to operate therobotic arm 414 of FIGS. 4A and 4B and/or the end effector. For sometasks, the robotic system 100 can calculate control sequences andsetting values for manipulating the robotic arm 414 and/or the endeffector and for moving the operation object 112 from the start location114 to the task location 116, as necessary via the shift location 118.The robotic system 100 can implement a control sequence mechanism (e.g.,a process, a function, an equation, an algorithm, acomputer-generated/readable model, or a combination thereof) configuredto calculate a movement path in space.

For example, the robotic system 100 can use A* algorithm, D* algorithm,and/or other grid-based searches so as to calculate the movement paththrough a space for moving the operation object 112 from the startlocation 114 to the task location 116 through one or more presentationposes/locations (e.g., one or more corresponding scan locations for theend effector), as necessary via the shift location 118. The controlsequence mechanism can transform the movement path into the sequence ofcommands or settings, or a combination thereof, for the actuationdevices 212 using a further process, function, or equation, and/or amapping table. In using the control sequence mechanism, the roboticsystem 100 can calculate the control sequence that will manipulate therobotic arm 414 and/or the end effector and cause the operation object112 to follow the calculated movement path.

The robotic system 100 can selectively create or derive a controlsequence based on the confidence measure. The robotic system 100 cancalculate the control sequence that includes an approach location (e.g.,the first approach location 432 of FIG. 4A and/or the second approachlocation 434 of FIG. 4A), one or more scan locations (e.g., the firstpresentation location 442 of FIG. 4A and/or the second presentationlocation 444 of FIG. 4A), or a combination thereof according to theconfidence measure. For example, the robotic system 100 can calculatethe approach location and/or the scan location according to a metric(e.g., a performance metric and/or a scan metric) based on an outcome ofcomparing the confidence measure to a sufficiency threshold. The scanlocation can be for placing the end effector so as to present one ormore surfaces of the operation object 112 before one or morecorresponding scanners 416 for scanning the one or more identifiers 332of the operation object 112 (by, e.g., placing/orienting the object inthe scanning field).

At block 532, the robotic system 100 can calculate (via, e.g., theprocessors 202) a set of available approach locations. The availableapproach locations can correspond to open or non-occupied spaces aboutthe start location 114 sufficient for placing the end effector. Inaddition, the robotic system 100 can place the end effector at aselected approach location for contacting and gripping the operationobject 112 without disturbing other operation objects 112.

For example, the robotic system 100 can calculate the set of availableapproach locations by calculating separation distances between theoutline of the operation object 112 and the outlines of adjacentoperation objects 112. The robotic system 100 can compare the separationdistances with a predetermined set of distances that correspond to aphysical size/shape of the end effector and/or various orientationsthereof. The robotic system can identify each of the available approachlocations in a case where the corresponding separation distances exceedthe predetermined set of distances corresponding to the size of the endeffector.

In decision block 534, the robotic system 100 can compare the confidencemeasure to one or more sufficiency thresholds to determine whether ornot the confidence measure is satisfied. In a case where the confidencemeasure satisfies the sufficiency threshold (e.g., when the confidencemeasure exceeds the required sufficiency threshold), as illustrated atblock 536, the robotic system 100 can calculate the control sequence(e.g., the first control sequence 422) based on a performance metric.When the confidence measure satisfies the sufficiency threshold, therobotic system 100 can determine that the initial pose is suitable andcalculate the control sequence without considering a scan metric thatcorresponds to a likelihood for scanning at least one identifier 332 ofthe operation object 112 and/or without considering a likelihood thatthe initial pose may be inaccurate.

As an example, the robotic system 100 can calculate candidate plans atblock 542. Each of the candidate plans can be an instance of a controlsequence that corresponds to a unique combination of an availableapproach location and a scan location (e.g., corresponding presentationlocation/orientation for the operation object 112). The robotic system100 can calculate the location 334 of the identifier 332 according tothe initial pose by rotating the location(s) 334 of the identifier 332or a corresponding model/pose in the master data 252. The robotic system100 can eliminate available approach locations that causes the endeffector to cover the location 334 of the identifier 332 (e.g., to beplaced directly over, in front of, and/or within a threshold distancefrom the location of the identifier).

The robotic system 100 can calculate a candidate plan for each remainingavailable approach location in the set (e.g., a calculation result ofblock 532). For each of the candidate plans, the robotic system 100 canfurther calculate a unique scan location according to the availableapproach location. The robotic system 100 can calculate the scanlocation based on rotating and/or moving a model of the operation object112, and thereby the surface corresponding to the location 334 of theidentifier 332 is in the scanning field and faces the correspondingscanner 416. The robotic system 100 can rotate and/or move the modelaccording to a predetermined process, equation, function, and the like.

At block 544, the robotic system 100 can calculate a performance metricfor each candidate plan. The robotic system 100 can calculate theperformance metric that corresponds to a throughput (rate) forcompleting the task 402. For example, the performance metric can beassociated with a movement distance of the operation object 112, anestimated movement duration, the number of commands and/or settingchanges for the actuation devices 212, a completion rate (e.g., a ratecomplementary to a piece-loss amount), or a combination thereof for thecandidate plan. The robotic system 100 can calculate the correspondingvalues for the candidate control sequence using one or more measured orknown data (e.g., an acceleration/speed associated withsettings/commands and/or piece-loss rate associated with a grip surfaceand/or a maneuver) and a predetermined calculation process, equation,function, and the like.

At block 546, the robotic system 100 can select the candidate plan withthe maximum performance metric (i.e., along with the correspondingapproach location) as the control sequence. For example, the roboticsystem 100 can select, as the control sequence, the candidate plan thatcorresponds to the highest completion rate, the shortest movementdistance, the lowest number of commands and/or setting changes, thefastest movement duration, or a combination thereof among the set ofcandidate plans. Accordingly, the robotic system 100 can select theavailable approach location in the set that corresponds to the highestperformance metric as the approach location.

In comparison, the robotic system 100 can calculate the candidate planaccording to a different measure in a case where the confidence measuredoes not satisfy the sufficiency threshold (e.g., the confidence measureis less than the required sufficiency threshold). As illustrated atblock 538, the robotic system 100 can calculate the control sequence(e.g., the second control sequence 424) based on a scan metric. The scanmetric is a value (e.g., a binary value or a non-binaryscore/percentage) that corresponds to a likelihood that at least one ofthe identifiers 332 of the operation object 112 remains uncovered by theend effector and is to be scannable, regardless of whether or not theinitial pose is accurate.

For example, the robotic system 100 can prioritize the scan metric(e.g., satisfy first and/or give it a heavier weight) over theperformance metric in a case where the confidence measure does notsatisfy the sufficiency threshold. Accordingly, the robotic system 100can calculate the control sequence that includes one or more scanlocations for providing (i.e., in the scanning field and/or facing thecorresponding scanner) at least one uncovered identifier 332 of theoperation object 112 in front of one or more scanners 416.

FIG. 5B is a flow diagram illustrating an example process flow of therobotic system in accordance with the embodiment of the presentdisclosure, and illustrates a flow diagram 538 for selectivelycalculating a control sequence (e.g., one or more locations for the endeffector) based on a scan metric.

In this example, calculating the control sequence based on a scan metriccan include calculating a set of locations of the exposed identifiers332 as illustrated in block 552. The robotic system 100 can calculatethe set of locations of the exposed identifiers 332 (e.g., the locations334 of the identifier 332 that remain uncovered or scannable with theend effector in the grip location) relative to the initial pose of theoperation object 112. The robotic system 100 can calculate the location334 of the exposed identifier 332 for each of the available approachlocations. The locations 334 of the exposed identifiers 332 cancorrespond to locations 334 of the identifiers 332 of the operationobject 112 that remain uncovered with the end effector at thecorresponding approach location according to a hypothesis that theinitial pose is accurate.

As described above for block 542, the master data 252 can include acomputer model or a template (e.g., offset measurements relative to oneor more edges and/or images of the operation object 112) in which thelocation 334 of the identifier 332 for each of the expected operationobjects 112 is described. The robotic system 100 can calculate the setof locations of the exposed identifiers 332 based on rotating and/ormoving the predetermined model/template in the master data 252 to matchthe initial pose. The robotic system 100 can eliminate the approachlocations that cause the end effector to cover the location 334 of theidentifier 332 (e.g., with the end effector placed directly over, infront of, and/or within a threshold distance from the location of theidentifier). In other words, the robotic system 100 can eliminate theavailable approach locations that are directly over, in front of, and/orwithin a threshold distance from the locations 334 of the identifiers332.

At block 554, the robotic system 100 can calculate a set of locations334 of alternative identifiers 332. The robotic system 100 can calculatethe set of locations 334 of the alternative identifiers 332 for posesalternative to the initial pose. For each of the available approachlocations, the robotic system 100 can calculate alternative poses, andfor each of the alternative poses, the robotic system 100 can calculatelocations of the alternative identifiers 332. Accordingly, the locationsof the alternative identifiers 332 can correspond to the locations 334of the identifiers 332 of the operation objects 112 that remainuncovered with the end effector at the corresponding approach locationaccording to a hypothesis that the initial pose is not accurate. Asdescribed above for the locations 334 of the exposed identifiers 332,the robotic system 100 can calculate the locations 334 of thealternative identifiers 332 based on rotating and/or moving thepredetermined model/template in the master data 252 according to thealternative pose.

At block 556, the robotic system 100 can calculate an exposurelikelihood for each of the approach locations, each of the alternativeposes, each of the identifiers 332 of the operation object 112, or acombination thereof. The exposure likelihood represents a likelihoodthat one or more identifiers of the operation object 112 remain exposedand scannable with the end effector gripping the operation object 112from the corresponding approach location. The exposure likelihood canrepresent both the scenario that the initial pose is accurate and thescenario that the initial pose is not accurate. In other words, theexposure likelihood can represent the likelihood that one or moreidentifiers of the operation object 112 remain exposed and scannableeven if the initial pose is inaccurate.

For example, the robotic system 100 can calculate the exposurelikelihood as a conditional certainty, such as a probabilistic valuecorresponding to a particular condition (e.g., a unique instance of theapproach location, the alternative pose, the identifier of the operationobject 112, or a combination thereof). The robotic system 100 cancalculate the exposure likelihood based on combining (via, e.g., addingand/or multiplying) the conditional certainty with acertainty/likelihood that the particular condition is true (e.g., avalue close to the confidence measure). The robotic system 100 cancalculate the exposure likelihood based on adding the certainty for eachof the identifiers considered to be exposed when multiple identifiersare exposed for the considered approach location and/or the consideredpose.

The robotic system 100 can calculate the exposure likelihood based oncombining the certainty values based on locations of the exposedidentifiers and locations of the alternative identifiers, for each ofpotential poses for a considered approach location. For example, therobotic system 100 can calculate the exposure likelihood using thecertainties for locations of the exposed identifiers and locations ofthe alternative identifiers with opposing signs (e.g., positive andnegative). The robotic system 100 can calculate the exposure likelihoodbased on adding the magnitudes of the two certainties and/or adding thecertainties with the signs. The overall magnitude can represent anoverall likelihood that one or more identifiers 332 of the operationobject 112 remain to be scannable, and the signed/vectored likelihoodcan represent a likelihood that one or more identifiers of the operationobject 112 remain to be scannable even if the initial pose wasinaccurate. Accordingly, an approach location would be ideal when theoverall magnitude is higher, and the signed/vectored likelihood iscloser to zero, such as representing similar chances that the identifier332 of the operation object 112 would be scannable regardless of theaccuracy for the initial pose.

At block 558, the robotic system 100 can select an approach location.The robotic system 100 can select, as the approach location, theavailable approach location that includes the location 334 of theuncovered identifier 332 in both a set of the exposed identifiers 332(e.g., a set of estimated locations of the identifiers 332 of theoperation object 112 according to a hypothesis that the initial pose isaccurate) and a set of the alternative identifiers 332 (e.g., one ormore sets of estimated locations of the identifiers 332 of the operationobject 112 according to a hypothesis that the initial pose is notaccurate). In other words, the robotic system 100 can select theapproach location that would leave at least one identifier 332 exposedand scannable regardless of the accuracy of the initial pose. Therobotic system 100 can select, as the approach location, the availableapproach location that corresponds to the exposure likelihood matchingand/or closest to a target condition, such as the largest overallmagnitude and/or the signed/vectored likelihood that is closer to zero.

The robotic system 100 can calculate a scan likelihood (e.g., alikelihood that an exposed identifier 332 of the operation object 112 issuccessfully scanned) based on the exposure likelihood. For example, therobotic system 100 can combine the exposure likelihood with anevaluation value (e.g., a tracked rate of successful scans, a physicalsize, and/or a type of the identifier 332) associated with thecorresponding exposed identifier 332 of the operation object 112. Therobotic system 100 can select, as the approach location, the availableapproach location that corresponds to the highest scan likelihood.

The robotic system 100 can compare the set of the exposed identifier 332to the set of the alternative identifier 332 to determine whether theset of the exposed identifier 332 and the set of the alternativeidentifier 332 include locations on opposing surfaces of the operationobject 112 (e.g., between the first pose 312 and the third pose 316).Accordingly, the robotic system 100 can select an available approachlocation that corresponds to a third surface (e.g., one of theperipheral surfaces 326 of the operation object 302) that is orthogonalto the two opposing surfaces.

At block 560, in a case where the confidence measure does not satisfythe sufficiency threshold, the robotic system 100 can create or derivecandidate control sequences based on the selected approach location. Therobotic system 100 can calculate the candidate control sequences thatinclude one or more scan locations for the end effector that correspondto one or more presentation locations/orientations for placing theidentifiers 332 of the operation object 112 in both the set of theexposed identifier 332 and the set of the alternative identifier 332. Inother words, the robotic system 100 can calculate the candidate controlsequences that can scan the operation object 112 regardless of theaccuracy of the initial pose.

The robotic system 100 can create or derive the candidate controlsequences that account for the locations 334 of the identifiers 332 inboth the set of the exposed identifier 332 and the set of thealternative identifiers 332. For example, the robotic system 100 cancalculate the candidate control sequences that account for the locationsof the identifiers 332 having a likelihood, on opposing and/ororthogonal surfaces. Accordingly, the robotic system 100 can account foran opposing pose (e.g., a pose oriented in an opposite direction wherethe outline of the operation object 112 is placed so as to be the samefrom a visual recognition location/angle) and/or other rotated poses inaddition to the initial pose. Referring back to FIG. 3A and FIG. 3C asan illustrative example, the robotic system 100 can calculate thecandidate control sequences that account for both the first pose 312 andthe third pose 316 in a case where the grip location corresponds to oneof the peripheral surfaces 326 of the operation object 302.

In order to account for multiple possible poses (e.g., erroneousestimation of the initial pose), the robotic system 100 can calculate ascanning pose for placing the identifiers 332 of the operation object112 in both the set of the exposed identifiers 332 and the set of thealternative identifiers 332. As illustrated at block 562, the roboticsystem 100 can calculate a set of candidate poses for the operationobject 112 in the scanning fields or through the scanning fields. Whenthe approach location is selected, the robotic system 100 can calculatecandidate scan locations as described above for block 542, such as byrotating and/or moving a model of the location 334 of the identifier 332so as to place the location 334 of the identifier 332 in the scanningfield.

At block 564, the robotic system 100 can map the set of the exposedidentifier 332 and the set of the alternative identifier 332 to each ofthe candidate scan locations. The robotic system 100 can map the set ofthe exposed identifier 332 based on rotating the model of the location334 of the identifier 332 starting from the initial pose. The roboticsystem 100 can map the set of the alternative identifier 332 based onrotating the model of the location 334 of the identifier 332 startingfrom one of the alternative poses (e.g., the opposing pose).

When the locations 334 of the identifiers 332 are mapped, at block 568,the robotic system 100 can compare the locations 334 and/or orientationsof the identifiers 332 of the operation object 112 in both the set ofthe exposed identifier 332 and the set of the alternative identifier 332with the scanning fields. At decision block 570, the robotic system 100can determine whether in the candidate pose, the identifiers 332 of theoperation object 112 in both the set of the exposed identifier 332 andthe set of the alternative identifier 332 simultaneously are presentedto the scanners.

At block 572, the robotic system 100 can identify, as the scanning pose,the candidate poses that simultaneously present the identifiers 332 ofthe operation object 112 in both the set of the exposed identifiers 332and the set of the alternative identifiers 332 to differentscanners/scanning fields. For example, in a case where the grip locationcorresponds to one of the peripheral surfaces 326 of the operationobject 112 with the locations of the operation object 112 in the set ofthe exposed identifier 332 and the set of the alternative identifier 332of the operation object 112 being on opposing surfaces, the roboticsystem 100 can identify the scanning pose for placing the operationobject 112 between a pair of opposing/facing scanners with each of theopposing surfaces of the operation object 112 facing one of thescanners.

At block 574, in a case where none of the candidate poses simultaneouslypresents the identifiers 332 of the operation object 112 in both the setof the exposed identifier 332 and the set of the alternative identifier332 of the operation object 112, the robotic system 100 can calculatemultiple scan locations (e.g., a first scan location and a second scanlocation) that each present at least one identifier 332 of the operationobject 112 from the set of the exposed identifier 332 and the set of thealternative identifier 332 of the operation object 112. For example, thefirst scan location can present the locations 334 of one or moreidentifiers 332 in the set of the identifiers 332 of the exposedoperation object 112 to one of the scanners, and the second scanlocation can present the locations 334 of one or more identifiers 332 inthe set of the identifiers 332 of the alternative operation object 112to one of the scanners. The second scan location can be associated withrotating the end effector about an axis, translating the end effector,or a combination thereof from the first scan location.

Referring back to the example illustrated in FIGS. 4A and 4B, the secondcontrol sequence 424 can correspond to the second approach location 434that corresponds to the third surface (e.g., one of the peripheralsurfaces 326 of the operation object 112) that is orthogonal to the twoopposing surfaces (e.g., for the first pose 312 and the third pose 316)as described above. Accordingly, the first scan location can correspondto one first location of the second presentation locations 444 thatplaces a surface (e.g., estimated to be the bottom surface 324 of theoperation object 112) corresponding to the initial pose (e.g., the firstpose 312) above an upward-facing scanner 416 and facing the scanner. Thesecond scan location can correspond to one second location of the secondpresentation locations 444 that rotates the operation object 112 by 90degrees in a counter-clockwise direction relative to an overall movementdirection (e.g., generally from the start location 114 to the tasklocation 116). Accordingly, the second scan location can correspond tothe second presentation location 444 that places a surface (e.g.,determined to be the bottom surface 324 of the operation object 112)corresponding to the alternative pose (e.g., the third pose 316) infront of a horizontally facing scanner 416 and in a vertical orientationfacing this scanner 416.

According to the resulting scanning pose and/or the set of scanlocations, the robotic system 100 can create or derive the candidatecontrol sequence. The robotic system 100 can calculate the candidateplans to place the end effector at the selected approach location,thereby to contact and grip the operation object 112, and lift and movethe operation object 112 to the identified scanning pose and/or the setof scan locations, using one or more mechanisms described above (e.g.,the A* mechanism). For example, when the scanning pose is identified,the robotic system 100 can calculate the candidate plans to establishthe scanning pose for the operation object 112 in the scanning fields orthrough the scanning fields. In a case where the robotic system 100 doesnot identify the scanning pose, the robotic system 100 can calculate thecandidate plans to move/orient the end effector sequentially through theset of multiple scan locations, thereby sequentially moving/rotating theoperation object 112 according to multiple presentationlocations/orientations.

At block 576, the robotic system 100 can create again or update thescanning likelihood for each of the candidate control sequences. Therobotic system 100 can update the scanning likelihood based on combiningthe various likelihoods and/or preferences as described above for block544 (e.g., probabilities and/or scores for the approach location, thescan location, the utilized scanner 416, the identifier 332 consideredto be exposed, an associated error and/or a loss rate, or a combinationthereof), but with respect to the scan metric instead of the performancemetric.

At block 578, the robotic system 100 can create or derive the controlsequence based on selecting the candidate plan according to the scanninglikelihood. The robotic system 100 can select, as the control sequence,the candidate plan that has maximum scanning likelihood among thecandidate plans. For example, the robotic system 100 can select thecandidate plan that has the highest likelihood of placing at least oneof the locations 334 of the exposed identifiers 332 and at least one ofthe locations 334 of the alternative identifiers 332 in one or morescanning fields (e.g., in front of one or more scanners 416) during themovement of the operation object 112 for scanning in the space betweenthe start location 114 and the task location 116, for example.

In a case where two or more candidate plans correspond to scanninglikelihoods within a relatively small difference value (e.g., apredetermined threshold), the robotic system 100 can calculate andevaluate the performance metric corresponding to the correspondingcandidate plan (e.g., as described above for blocks 544 and 546). Therobotic system 100 can select, as the control sequence, the candidateplan that is closest to the target condition.

The robotic system 100 can deviate from the illustrated example flow.For example, the robotic system 100 can select the approach location asdescribed above. Based on the selected approach location, the roboticsystem 100 can grip the operation object 112 and implement apredetermined set of maneuvers, such as to lift, reorient, horizontallymove, place back down and release, or a combination thereof. During orafter the predetermined set of maneuvers, the robotic system 100 canre-image or scan the pick-up area (via, e.g., looping back to block 502)and redetermine the initial pose and the confidence measure (via, e.g.,block 522 and block 524).

Returning back to FIG. 5A, at block 508, the robotic system 100 canbegin implementing the resulting control sequence. The robotic system100 can implement the control sequence based on operating the one ormore processors 202 to send the commands and/or settings of the controlsequence to other devices (e.g., the corresponding actuation devices 212and/or other processors) to execute the tasks 402 and 404. Accordingly,the robotic system 100 can execute the control sequence by operating theactuation devices 212 according to the sequence of commands or settingsor combination thereof. For example, the robotic system 100 can operatethe actuation devices 212 to dispose the end effector at the approachlocation around the start location 114, contact and grip the operationobject 112, or perform a combination thereof.

At block 582, the robotic system 100 can move the end effector to thescan location, thereby moving the operation object 112 to thepresentation location/orientation. For example, after or along withlifting the operation object 112 from the start location 114, therobotic system 100 can move the end effector to establish the scanningpose for the operation object 112. In addition, the robotic system 100can move the end effector to the first scan location.

At block 584, the robotic system 100 can operate the scanners 416 toscan the operation object 112. For example, one or more processors 202can send a command to the scanners 416 to implement a scan and/or send aquery to the scanners 416 to receive a scan status and/or a scannedvalue. At block 585 and the like, in a case where the control sequenceincludes the scanning pose, the robotic system 100 can implement thecontrol sequence to move the operation object 112 in the scanning poseacross the scanning fields in a direction orthogonal to orientations ofthe scanning fields. While the operation object 112 is moved, thescanners 416 can (simultaneously and/or sequentially) scan multiplesurfaces for multiple possible locations 334 of the identifier 332 ofthe operation object 112.

In decision block 586, the robotic system 100 can evaluate the scanresult (e.g., status and/or the scanned value) to determine whether theoperation object 112 is scanned. For example, the robotic system 100 canverify the scan result after implementing the control sequence up to thefirst scan location. At block 588 and the like, in a case where the scanresult indicates a successful scan of the operation object 112 (e.g.,the status corresponds to detection of a valid code/identifier and/orthe scanned value matches the identified/expected operation object 112),the robotic system 100 can move the operation object 112 to the tasklocation 116. Based on the successful scan, the robotic system 100 canignore any subsequent scan location (e.g., the second scan location) anddirectly move the operation object 112 to the task location 116.

In a case where the scan result indicates an unsuccessful scan of theoperation object 112, the robotic system 100 can determine at decisionblock 590 whether the current scan location is the last one in thecontrol sequence. In a case where it is not the last control sequence,the robotic system 100 can move the operation object 112 to the nextpresentation location/orientation as represented by a loop back to block582.

In a case where the current scan location is the last one in the controlsequence, the robotic system 100 can implement one or more remedialactions as illustrated at block 592. The robotic system 100 can stopand/or cancel the control sequence in a case where the scan results forall of the scan locations in the control sequence indicate failed scans.The robotic system 100 can generate an error status/message fornotifying an operator. The robotic system 100 can place the operationobject 112 inside of an area (i.e., at a location different from thestart location 114 and the task location 116) designated for theoperation object 112 that failed to be scanned.

Based on either successfully completing the tasks 402 and 404 (i.e.,successfully scanning the operation object 112 and placing the operationobject at the task location 116) or implementing the remedial actions,the robotic system 100 can move on to the next task/operation object112. The robotic system 100 can scan again the designated area asillustrated by a loop back to block 502 and select the next operationobject 112 using the existing imaging data as illustrated by a loop backto block 504.

Scanning the operation object 112 in the air (e.g., at a locationbetween the start location 114 and the task location 116) providesimproved efficiency and speed for performing the tasks 402 and 404. Bycalculating the control sequence for cooperating with the scanner 416 asthe control sequence that includes the scan locations, the roboticsystem 100 can effectively combine the task for moving the operationobject 112 with the task for scanning the operation object 112.Moreover, creating or deriving a control sequence according to theconfidence measure of the initial pose further improves efficiency,speed, and accuracy for the scan task. As described above, the roboticsystem 100 can create or derive the control sequence for accounting foralternative orientations that correspond to the scenario of the initialpose being inaccurate. Accordingly, the robotic system 100 can increasethe likelihood of accurately/successfully scanning the operation object112 even with pose determination errors, such as due to calibrationerrors, unexpected poses, unexpected lighting conditions, and the likeThe increased likelihood in accurate scans can lead to increased overallthroughput for the robotic system 100 and further reduce operatorefforts/interventions.

Concise Description of Various Embodiments

Embodiment 1 relates to a method for controlling a robotic system. Themethod comprises: deriving an approach location, the approach locationrepresenting a location of an end effector for gripping an operationobject having an identification information; deriving a scan location,the scan location representing a location of a scanner for scanning theidentification information of the operation object; and deriving, basedon the approach location and the scan location, a control sequence forinstructing a robot to execute the control sequence. The controlsequence includes: gripping the operation object at a start location;scanning the identification information of the operation object with thescanner; temporarily releasing the operation object from the endeffector at a shift location and regripping the operation object by theend effector to be shifted at the shift location, when a predeterminedcondition is satisfied, wherein the predetermined condition isassociated with a storage efficiency; and moving the operation objectfrom the shift location to a task location when the predeterminedcondition is satisfied. The task location is different from the startlocation and the shift location.

Embodiment 2 includes the method of embodiment 1. In this embodiment,the method further comprises: calculating a first storage efficiency atthe task location, wherein the first storage efficiency represents astorage efficiency before the operation object is shifted at the shiftlocation; calculating a second storage efficiency at the task location,wherein the second storage efficiency represents a storage efficiencyafter the operation object is shifted at the shift location; determiningthat the second storage efficiency is more efficient the first storageefficiency; and determining, based on the second storage efficiency,that the predetermined condition is satisfied.

Embodiment 3 includes the method of embodiment 2. In this embodiment,the method further comprises deriving a height of the operation object,wherein calculating the first storage efficiency includes calculatingthe first storage efficiency based on the height of the operationobject, and wherein calculating the second storage efficiency includescalculating the second storage efficiency based on the height of theoperation object.

Embodiment 4 includes the method of embodiment 3. In this embodiment,the method further comprises measuring a height location of a bottomsurface of the operation object while the operation object is gripped,wherein deriving the height of the operation object includes calculatingthe height of the operation object from a height location of a topsurface of the operation object and the measured height location of thebottom surface of the operation object.

Embodiment 5 includes the method of embodiment 4. In this embodiment,measuring the height location of the bottom surface of the operationobject includes measuring the height location of the bottom surface ofthe operation object while the identification information of theoperation object is scanned.

Embodiment 6 includes the method of any one of embodiments 1-5. In thisembodiment, deriving the control sequence for instructing the robot toexecute the control sequence includes temporarily releasing theoperation object from the end effector by placing the operation objecton a temporary placing table disposed at the shift location.

Embodiment 7 includes the method of any one of embodiments 1-6. In thisembodiment, the method further comprises: deriving imaging datarepresenting a pick-up area including the operation object; determiningan initial pose of the operation object based on the imaging data;calculating a confidence measure indicating a likelihood that theinitial pose of the operation object is accurate; and deriving theapproach location and the scan location based on the confidence measure.

Embodiment 8 relates to a robotic system. The robotic system comprises:at least one processor, and at least one memory device connected to theat least one processor. The at least one memory device includesinstructions thereon that, when executed by the at least one processor,cause the at least one processor for:

deriving an approach location, the approach location defining a locationfor an end effector to grip an operation object having an identificationinformation;

deriving a scan location, the scan location defining a location for ascanner to scan the identification information of the operation object;and

deriving, based on the approach location and the scan location, acontrol sequence to instruct a robot to execute the control sequence.The control sequence includes: gripping the operation object at a startlocation; scanning the identification information of the operationobject with the scanner; temporarily releasing the operation object fromthe end effector at a shift location and regripping the operation objectby the end effector to be shifted at the shift location, when apredetermined condition is satisfied, wherein the predeterminedcondition is associated with a storage efficiency; and moving theoperation object from the shift location to a task location when thepredetermined condition is satisfied. The task location is differentfrom the start location and the shift location.

Embodiment 9 includes the robotic system of embodiment 8. In thisembodiment, the at least one memory device further includes instructionsthat cause the at least one processor for:

calculating a first storage efficiency at the task location, the firststorage efficiency associated with a storage efficiency before theoperation object is shifted at the shift location;

calculating a second storage efficiency at the task location, the secondstorage efficiency associated with a storage efficiency after theoperation object is shifted at the shift location;

determining that the second storage efficiency is more efficient thanthe first storage efficiency; and

determining, based on the second storage efficiency, that thepredetermined condition is satisfied.

Embodiment 10 includes the robotic system of embodiment 9. In thisembodiment, the at least one memory device further includes instructionsthat cause the at least one processor for deriving a height of theoperation object, wherein calculating the first storage efficiencyincludes calculating the first storage efficiency based on the height ofthe operation object, and wherein calculating the second storageefficiency includes calculating the second storage efficiency based onthe height of the operation object.

Embodiment 11 includes the robotic system of embodiment 10. In thisembodiment, the at least one memory device further includes instructionsthat cause the at least one processor for measuring a height location ofa bottom surface of the operation object while the operation object isgripped by the end effector, wherein deriving the height of theoperation object includes calculating the height of the operation objectfrom a height location of a top surface of the operation object and themeasured height location of the bottom surface of the operation object.

Embodiment 12 includes the robotic system of embodiment 11. In thisembodiment, measuring the height location of the bottom surface of theoperation object includes measuring the height location of the bottomsurface of the operation object while the identification information ofthe operation object is scanned with the scanner.

Embodiment 13 includes the robotic system of any one of embodiments8-12. In this embodiment, deriving the control sequence for instructingthe robot to execute the control sequence includes temporarily releasingthe operation object from the end effector by placing the operationobject on a temporary placing table disposed at the shift location.

Embodiment 14 relates to a tangible, non-transitory computer-readablemedium having processor instructions stored thereon. When executed by atleast one processor thereof, the processor instructions cause therobotic system. The processor instructions comprise:

deriving an approach location, the approach location representing alocation for an end effector for gripping an operation object having anidentification information;

deriving a scan location, the scan location representing a location of ascanner for scanning the identification information of the operationobject; and

deriving, based on the approach location and the scan location, acontrol sequence for instructing a robot to execute the controlsequence. The control sequence includes: gripping the operation objectat a start location; scanning the identification information of theoperation object with the scanner; temporarily releasing the operationobject from the end effector at a shift location and regripping theoperation object by the end effector to be shifted at the shiftlocation, when a predetermined condition is satisfied, wherein thepredetermined condition is associated with a storage efficiency; andmoving the operation object from the shift location to a task locationwhen the predetermined condition is satisfied. The task location isdifferent from the start location and the shift location.

Embodiment 15 includes the tangible, non-transitory computer-readablemedium of embodiment 14. In this embodiment, the processor instructionsfurther comprise:

calculating a first storage efficiency at the task location, the firststorage efficiency representing a storage efficiency before theoperation object is shifted at the shift location;

calculating a second storage efficiency at the task location, the secondstorage efficiency representing a storage efficiency after the operationobject is shifted at the shift location;

determining that the second storage efficiency is more efficient thanthe first storage efficiency; and

determining, based on the second storage determination, that thepredetermined condition is satisfied.

Embodiment 16 includes the tangible, non-transitory computer-readablemedium of embodiment 15. In this embodiment, calculating the firststorage efficiency includes calculating the first storage efficiencybased on the height of the operation object, and calculating the secondstorage efficiency includes calculating the second storage efficiencybased on the height of the operation object.

Embodiment 17 includes the tangible, non-transitory computer-readablemedium of embodiment 16. In this embodiment, the processor instructionsfurther comprise measuring a height location of a bottom surface of theoperation object while the operation object is gripped by the endeffector, wherein deriving the height of the operation object includescalculating the height of the operation object from a height location ofa top surface of the operation object and the measured height locationof the bottom surface of the operation object.

Embodiment 18 includes the tangible, non-transitory computer-readablemedium of embodiment 17. In this embodiment, measuring the heightlocation of the bottom surface of the operation object includesmeasuring the height location of the bottom surface of the operationobject while the identification information of the operation object isscanned.

Embodiment 19 includes the tangible, non-transitory computer-readablemedium of any one of embodiments 14-18. In this embodiment, deriving thecontrol sequence for instructing the robot to execute the controlsequence includes temporarily releasing the operation object from theend effector by placing the operation object on a temporary placingtable disposed at the shift location.

CONCLUSION

The above Detailed Description of examples of the present disclosure isnot intended to be exhaustive or to limit the present disclosure to theprecise form disclosed above. While specific examples for the presentdisclosure are described above for illustrative purposes, variousequivalent modifications are possible within the scope of the presentdisclosure, as those skilled in the relevant art will recognize. Forexample, while processes or blocks are provided in a given order,alternative implementations may implement routines having steps, oremploy systems having blocks, in a different order, and some processesor blocks may be deleted, moved, added, subdivided, combined, and/ormodified to provide an alternative or subsidiary combination. Each ofthese processes or blocks may be implemented in a variety of differentways. Also, while processes or blocks are at times shown as beingimplemented in series, these processes or blocks may instead beimplemented or executed in parallel, or may be implemented at differenttimes. Further, any specific numbers noted herein are only examples;alternative implementations may employ different values or ranges.

These and other changes can be made to the present disclosure in lightof the above Detailed Description. While the Detailed Descriptiondescribes certain examples of the present disclosure as well as the bestmode contemplated, the present disclosure can be practiced in many ways,no matter how detailed the above description appears in text. Details ofthe system may change considerably in its specific implementation, whilestill being encompassed by the present disclosure. As noted above,particular terminology used when describing certain features or aspectsof the present disclosure should not be taken to imply that theterminology is redefined herein to be restricted to any of specificcharacteristics, features, or aspects of the present disclosure withwhich the terminology is associated. Accordingly, the invention is notlimited, except as by the appended claims. In general, the terms used inthe following claims should not be construed to limit the presentdisclosure to the specific examples disclosed in the specification,unless the above Detailed Description section explicitly defines suchterms.

Although certain aspects of the invention are presented below in certainclaim forms, the applicant contemplates the various aspects of theinvention in any number of claim forms. Accordingly, the applicantreserves the right to pursue additional claims after filing thisapplication to pursue such additional claim forms, in either thisapplication or in a continuing application.

What is claimed is:
 1. A control method of a robotic system that includes a robot having a robotic arm and an end effector, the method comprising: deriving an approach location at which the end effector grips an operation object; and based on the approach location, creating or deriving a control sequence to instruct the robot to execute the control sequence, wherein the control sequence includes: temporarily releasing the operation object from the end effector and regripping the operation object by the end effector to be shifted, at a shift location, when a predetermined condition is satisfied; and moving the operation object to the task location.
 2. The control method of claim 1, wherein the predetermined condition is based on a storage efficiency of the operation object at the task location.
 3. The control method of claim 2, wherein the storage efficiency is based on a height of the operation object.
 4. The control method of claim 3, further comprising calculating the height of the operation object from a height location of a top surface of the operation object and a height location of a bottom surface of the operation object measured in a state of being gripped by the end effector.
 5. The control method of claim 1, further comprising: obtaining imaging data representative of a pick-up area including the operation object; determining an initial pose of the operation object based on the imaging data; calculating a confidence measure in relation to an accuracy of the initial pose of the operation object; and deriving the approach location based on the confidence measure.
 6. The control method of claim 5, wherein the control sequence further includes selectively calculating the approach location according to a performance metric and/or a scan metric, based on a result of comparing the confidence measure to a sufficiency threshold.
 7. The control method of claim 6, wherein for a situation where the confidence measure does not satisfy the sufficiency threshold, the approach location are derived based on the scan metric or are derived based on the scan metric with prioritizing the scan metric over the performance metric.
 8. The control method of claim 6, wherein for a situation where the confidence measure satisfies the sufficiency threshold, the approach location are derived based on the performance metric.
 9. The control method of claim 1, wherein the control sequence further includes: deriving a first scan location for providing identification information of the operation object to a scanner; deriving a second scan location for providing alternative identification information of the operation object to the scanner; and moving the operation object (1) to the task location and ignoring the second scan location in a situation where a scan result indicates a successful scan after the operation object is moved to the first scan location, or (2) to the second scan location when a scan result indicates a failed scan after the operation object is moved to the first scan location.
 10. A non-transitory computer-readable medium storing processor instructions for controlling a robotic system that includes a robot having a robotic arm and an end effector, the processor instructions causing, when executed by one or more processors, the robotic system to: derive an approach location at which the end effector grips an operation object; and create or derive a control sequence to instruct the robot to execute the control sequence based on the approach, wherein the control sequence includes: temporarily releasing the operation object from the end effector and regripping the operation object by the end effector to be shifted, at a shift location, when a predetermined condition is satisfied; and moving the operation object to the task location.
 11. The non-transitory computer-readable medium of claim 10, wherein the predetermined condition is based on a storage efficiency of the operation object at the task location.
 12. The non-transitory computer-readable medium of claim 11, wherein the storage efficiency is based on a height of the operation object.
 13. The non-transitory computer-readable medium of claim 11, wherein the robotic system is further caused to calculate a height of the operation object from a height location of a top surface of the operation object and a height location of a bottom surface of the operation object measured in a state of being gripped by the end effector.
 14. The non-transitory computer-readable medium of claim 13, wherein the control sequence further includes: deriving a first scan location for providing identification information of the operation object to a scanner; deriving a second scan location for providing alternative identification information of the operation object to the scanner; and generating commands and/or settings to move the operation object (1) to the task location and ignoring the second scan location in a situation where a scan result indicates a successful scan after the operation object is moved to the first scan location, or (2) to the second scan location when a scan result indicates a failed scan after the operation object is moved to the first scan location.
 15. A controller of a robotic system that includes a robot having a robotic arm and an end effector, the controller comprising: a communication device configured to communicate with a remote device; and a processor coupled to the communication device, wherein the processor is configured to execute a control method, the control method comprising: deriving an approach location at which the end effector grips an operation object; and based on the approach location, creating or deriving a control sequence to instruct the robot to execute the control sequence, wherein the control sequence includes: temporarily releasing the operation object from the end effector and regripping the operation object by the end effector to be shifted, at a shift location, when a predetermined condition is satisfied; and moving the operation object to the task location.
 16. The controller of claim 15, wherein the predetermined condition is based on a storage efficiency of the operation object at the task location.
 17. The controller of claim 16, wherein the storage efficiency is based on a height of the operation object.
 18. The controller of claim 15, wherein the control method further includes: obtaining imaging data representative of a pick-up area including the operation object; determining an initial pose of the operation object based on the imaging data; calculating a confidence measure in relation to an accuracy of the initial pose of the operation object; and deriving the approach location based on the confidence measure.
 19. The controller of claim 15, wherein the control sequence further includes: deriving a first scan location for providing identification information of the operation object to a scanner; deriving a second scan location for providing alternative identification information of the operation object to the scanner; and generating commands and/or settings to move the operation object to the task location while bypassing the second scan location when a scan result indicates a successful scan after the operation object is moved to the first scan location.
 20. The controller of claim 15, wherein the control sequence further includes: deriving a first scan location for providing identification information of the operation object to a scanner; deriving a second scan location for providing alternative identification information of the operation object to the scanner; and generating commands and/or settings to move the operation object (1) to the task location and ignoring the second scan location in a situation where a scan result indicates a successful scan after the operation object is moved to the first scan location, or (2) to the second scan location when a scan result indicates a failed scan after the operation object is moved to the first scan location. 